Public Sentiment and the State Standards
Unit 3 Case Study
Dr. Shaun Kellogg
October 25, 2021
1. PREPARE
Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg’s recent article, Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.
We will focus on conducting a very simplistic “replication study” by comparing the sentiment of tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand public reaction to these two curriculum reform efforts. I highly recommend you watch the quick 3-minute overview of this work at <https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x>. Specifically, our Unit 3 case study will cover the following topics:
- Prepare: We’ll take a quick look at Dr. Rosenberg’s study, load packages we’ll need for analysis, and learn how to use Twitter’s API to retrieve data for those that created a Twitter Developer account.
- Wrangle: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to “tidy text” so we can perform some basic analyses such as retrieving word counts and term frequencies.
- Explore: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
- Model: We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.
Communicate:
1a. Review the Literature
The Unit 3 Case Study: Public Sentiment and the State Standards is guided by a recent publication by (Rosenberg et al., 2021) Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. This study in turn builds on upon previous work by Wang & Fikis (2017) examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the Next Generation Science Standards (NGSS) and Common Core State Standards (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.
Abstract
System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (N = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time—and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.
Data Sources
Similar to data we’ll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Data used in this case was pulled using an Academic Research developer account and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: #ccss, common core, #ngsschat, ngss. Below is an example of the code used to retrieve data for this lab. This code is set not to execute and will not run, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.
library(academictwitteR)
library(tidyverse)
ccss_tweets_2021 <-
get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
"2021-01-01T00:00:00Z",
"2021-05-31T00:00:00Z",
bearer_token,
data_path = "ccss-data/",
bind_tweets = FALSE)
ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
select(text,
created_at,
author_id,
id,
conversation_id,
source,
possibly_sensitive,
in_reply_to_user_id)
write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))Analysis
The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We’ll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.
The authors also used the lme4 package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won’t try to replicate in this study, but we will take a look at some of their findings from this model in below.
Summary of Key Findings
- Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
- Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
- Differences between the context of the tweets were small, but those that did not include the #NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
- Individuals posted more tweets during #NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the #NGSSchat was positive.
Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at <https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x>
1b. Define Questions
One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we’ll explore later in this case study, is the question:
How do we to quantify what a document or collection of documents is about?
The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:
- What is the public sentiment expressed toward the NGSS?
- How does sentiment for teachers differ from non-teachers?
- How do tweets posted to #NGSSchat differ from those without the hashtag?
- How does participation in #NGSSchat relate to the public sentiment individuals express?
- How does public sentiment vary over time?
For our text mining case study, we’ll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we’ll attempt to answer the following questions:
- What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
- How does sentiment for NGSS compare to sentiment for CCSS?
1c. Load Libraries
tidytext 📦
As we’ll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.
Let’s go ahead and load the {tidytext} package:
library(tidytext)For a more comprehensive introduction to the tidytext package, I cannot recommend enough the free and excellent online book, Text Mining with R: A Tidy Approach (Silge & Robinson, 2017). If you’re interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.
The vader Package 📦
The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.
To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text.
Let’s go ahead and load the VADER library:
library(vader)Note: The {vader} package can take quite some time to run on a large datasets like the one we’ll be working with, so in our Model section we will examine just a small(ish) subset of tweets.
rtweet 📦 (Optional)
The {rtweet} package provides users a range of functions designed to extract data from Twitter’s REST and streaming APIs and has three main goals:
Formulate and send requests to Twitter’s REST and stream APIs.
Retrieve and iterate over returned data.
Wrangling data into tidy structures.
For those that created a Twitter Developer account, load the {rtweet} package that we’ll be using to accomplish all three of the goals listed above:
library(rtweet)Other Packages
Finally, there are a few other packages we’ll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.3 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x purrr::flatten() masks rtweet::flatten()
## x dplyr::lag() masks stats::lag()library(here)## here() starts at /Volumes/GoogleDrive/My Drive/College of Ed/Learning Analytics/Courses/ECI 586 Intro to LA/GitHub/eci-586library(wordcloud2)2. WRANGLE
The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled (Krumm et al., 2018). This case study will place a heavy emphasis on preparing text for analysis and in particular we’ll learn how to:
- Import Tweets. First we revisit the familiar
read_csv()function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we’ll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter. - Restructure Data. We focus on removing extraneous data using the
select()andfilter()functions from {dplyr}, and revisit functions from the Tidy Your Data Primer for merging data frames. - Tidy Text. Finally, we introduce the {tidytext} package to “tidy” and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove “stop words” that don’t add much value to our analysis.
2a. Part 1: Import Tweets from API (Optional)
This section is optional and for those those setup a Twitter Developer Account and App. The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:
create_token()Sends request to generate authorization tokens for use of the Twitter API.search_tweets()Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms.search_tweets2()Returns data from multiple search queries.get_timelines()Returns up to 3,200 tweets of one or more specified Twitter users.
Authorization methods
Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.
- Navigate to developer.twitter.com/en/apps and select your Twitter app
- Click the tab labeled
Keys and tokensto retrieve your keys. - Locate the
Consumer API keys(aka “API Secret”).
- Scroll down to
Access token & access token secretand clickCreate
- Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to
create_token(). Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"
## authenticate via web browser
token <- create_token(
app = app_name,
consumer_key = api_key,
consumer_secret = api_secret_key,
access_token = access_token,
access_secret = access_token_secret)Search Tweets
Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let’s begin by introducing the search_tweets() function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame ngss_all_tweets.
Type or copy the following code into your R script or console and run:
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)Note that the first argument q = that the search_tweets() function expects is the search term included in quotation marks and that n = specifies the maximum number of tweets
Your Turn ⤵
View your new ngss_all_tweetsdata frame using the code chunk below and answer the following questions:
ngss_all_tweets## # A tibble: 276 × 90
## user_id status_id created_at screen_name text source
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 28456376 145251555… 2021-10-25 06:01:09 aeryn_thra… "PLS R… Twitt…
## 2 1331611799053955073 145243945… 2021-10-25 00:58:44 B0tSci "An ex… SciBot
## 3 1331611799053955073 145133305… 2021-10-21 23:42:20 B0tSci "Over … SciBot
## 4 46152623 145243942… 2021-10-25 00:58:39 PraxisMedH… "An ex… Twitt…
## 5 147095974 145240435… 2021-10-24 22:39:17 doctora_na… "PLS R… Twitt…
## 6 2592073856 145239443… 2021-10-24 21:59:52 STEMTeachT… "REFLE… Twitt…
## 7 2592073856 144986902… 2021-10-17 22:44:47 STEMTeachT… "So ri… Twitt…
## 8 2592073856 144986890… 2021-10-17 22:44:19 STEMTeachT… "Check… Twitt…
## 9 2592073856 144941157… 2021-10-16 16:27:02 STEMTeachT… "So ma… Twitt…
## 10 2592073856 144987005… 2021-10-17 22:48:52 STEMTeachT… "As we… Twitt…
## # … with 266 more rows, and 84 more variables: display_text_width <dbl>,
## # reply_to_status_id <chr>, reply_to_user_id <chr>,
## # reply_to_screen_name <chr>, is_quote <lgl>, is_retweet <lgl>,
## # favorite_count <int>, retweet_count <int>, quote_count <int>,
## # reply_count <int>, hashtags <list>, symbols <list>, urls_url <list>,
## # urls_t.co <list>, urls_expanded_url <list>, media_url <list>,
## # media_t.co <list>, media_expanded_url <list>, media_type <list>, …How many tweets did our query using the Twitter API actually return? How many variables?
Why do you think our query pulled in far less than 5,000 tweets requested?
Does our query also include retweets? How do you know?
Remove Retweets
While not explicitly mentioned in the paper, it’s likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else’s tweet and would duplicate the exact same content of the original.
Let’s use the include_rts = argument to remove any retweets by setting it to FALSE:
ngss_non_retweets <- search_tweets("#NGSSchat",
n=5000,
include_rts = FALSE)Using the OR Operator
If you recall from the Data Sources section above, the authors accessed tweets and user information from the hashtag-based #NGSSchat online community, all tweets that included any of the following phrases, with “/” indicating an additional phrase featuring the respective plural form: “ngss,” “next generation science standard/s,” “next gen science standard/s.”
Let’s modify our query using the OR operator to also include “ngss” so it will return tweets containing either #NGSSchat or “ngss” and assign to ngss_or_tweets:
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss",
n=5000,
include_rts = FALSE)Your Turn ⤵
Try including both search terms but excluding the OR operator to answer the following question:
ngss_both_tweets <- search_tweets(q = "#NGSSchat ngss",
n=5000,
include_rts = FALSE)Does excluding the
ORoperator return more tweets, the same number of tweets, or fewer tweets? Why?What other useful arguments does the
search_tweet()function contain? Try adding one and see what happens.
Hint: Use the ?search_tweets help function to learn more about the q argument and other arguments for composing search queries.
Use Multiple Queries
Unfortunately, the OR operator will only get us so far. In order to include the additional search terms, we will need to use the c() function to combine our search terms into a single list.
The rtweets package has an additional search_tweets2() function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., q = '"next gen science standard"' or escape each internal double quote with a single backslash, e.g., q = "\"next gen science standard\"".
Copy and past the following code to store the results of our query in ngss_tweets:
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"' ),
n=5000,
include_rts = FALSE)Our First Dictionary
Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let’s go ahead and create our very first “dictionary” for identifying tweets related to either set of standards, and then use that dictionary for our the q = query argument to pull tweets related to the state standards.
To do so, we’ll need to add some additional search terms to our list:
ngss_dictionary <- c("#NGSSchat OR ngss",
'"next generation science standard"',
'"next generation science standards"',
'"next gen science standard"',
'"next gen science standards"')
ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)Now let’s create a dictionary for the Common Core State Standards and pass that to our search_tweets() function to get the most recent tweets:
ccss_dictionary <- c("#commoncore", '"common core"')
ccss_tweets <-
ccss_dictionary %>%
search_tweets2(n=5000, include_rts = FALSE)Notice that you can use the pipe operator with the search_tweets() function just like you would other functions from the tidyverse.
Your Turn ⤵
- Use the
search_tweetsfunction to create you own custom query for a twitter hashtag or topic(s) of interest.
Other Useful Queries
For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!
For example, let’s create another list containing the usernames of me and some of my colleagues at the Friday Institute using the c() function again and use the get_timelines() function to get the most recent tweets from each of those users:
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier")
fi_tweets <- fi %>% get_timelines(include_rts=FALSE)And let’s use the sample_n() function from the dplyr package to pick 10 random tweets and use select() to select and view just the screenname and text columns that contains the user and the content of their post:
sample_n(fi_tweets, 10) %>% select(screen_name, text)## # A tibble: 10 × 2
## screen_name text
## <chr> <chr>
## 1 AlexDreier "The AP blithely referring to Robert E. Lee as a \"hero\" de…
## 2 haspires "Dr. Bridges: \"We want Black and Brown students to live out…
## 3 sbkellogg "@ry_estrellado Pretty sure it’s Frog who’s the well-adjuste…
## 4 haspires "So enjoyed learning from these scholars today about their C…
## 5 AlexDreier "Literally spit out my water at this tweet. https://t.co/0be…
## 6 haspires "Leylah!"
## 7 sbkellogg "@minebocek @jrosenberg6432 @cdhowe I know the answer likely…
## 8 drcallie_tweets "@DrTawannahAllen @NikkiSasser14 I agree, this is a fantasti…
## 9 tarheel93 "@lauraalbrecht22 @DE_AmyS 😘 Booked through the end of 2020…
## 10 AlexDreier "@robmoore3 Literal lol"We’ve only scratched the surface of the number of functions available in the rtweets package for searching Twitter. Use the following function to learn more about the {retwee} package:
vignette("intro", package="rtweet")Your Turn ⤵
To conclude Section 2a, try one of the following search functions from the rtweet vignette:
get_timelines()Get the most recent 3,200 tweets from users.stream_tweets()Randomly sample (approximately 1%) from the live stream of all tweets.get_friends()Retrieve a list of all the accounts a user follows.get_followers()Retrieve a list of the accounts following a user.get_favorites()Get the most recently favorited statuses by a user.get_trends()Discover what’s currently trending in a city.search_users()Search for 1,000 users with the specific hashtag in their profile bios.
2a. Part 2: Import Tweets from CSV
First, let’s use the by now familiar read_csv() and here() functions to import our ccss_tweets.csv file saved in our data folder:
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)## Warning: One or more parsing issues, see `problems()` for detailsNote the addition of the col_types = argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:
author_id= the author of the tweetid= the unique id for each tweetconverastion_id= the unique id for each conversation threadin_reply_to_user_id= the author of the tweet being replied to
Your Turn ⤵
Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"),
col_types = cols(author_id = col_character(),
id = col_character(),
conversation_id = col_character(),
in_reply_to_user_id = col_character()
)
)Importing data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an “Import Dataset” feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.
Now use the following code chunk to inspect the head() of each data frame and answer the questions that follow:
head(ngss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "Please help u… 2021-01-06 00:50:49 3279907796 13466… 13466201998945… Twitte…
## 2 "What lab mate… 2021-01-06 00:45:32 1010324664… 13466… 13466188701325… Hootsu…
## 3 "I recently sa… 2021-01-06 00:39:37 61829645 13466… 13466173820858… Twitte…
## 4 "I'm thrilled … 2021-01-06 00:30:13 461653415 13466… 13466150172071… Twitte…
## 5 "PLS RT. Excit… 2021-01-06 00:15:05 22293234 13466… 13466112069671… Twitte…
## 6 "Inspired by M… 2021-01-06 00:00:00 3317960226 13466… 13466074140999… TweetD…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>head(ccss_tweets)## # A tibble: 6 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@catturd2 Hmm… 2021-01-02 00:49:28 1609854356 13451… 13451697062071… Twitte…
## 2 "@homebrew1500… 2021-01-02 00:40:05 1249594897… 13451… 13451533915976… Twitte…
## 3 "@ClayTravis D… 2021-01-02 00:32:46 8877070540… 13451… 13450258639942… Twitte…
## 4 "@KarenGunby @… 2021-01-02 00:24:01 1249594897… 13451… 13451533915976… Twitte…
## 5 "@keith3048 I … 2021-01-02 00:23:42 1252747591 13451… 13451533915976… Twitte…
## 6 "Probably comm… 2021-01-02 00:18:38 1276017320… 13451… 13451625486818… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?
- CSSS
Let’s take a slightly larger sample of the CCSS tweets:
ccss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "How many peo… 2021-05-20 23:32:59 1341369281… 13955… 13955229731681… Twitte…
## 2 "@DrKarlynB O… 2021-03-21 19:54:42 1163405230… 13737… 13737243823302… Twitte…
## 3 "@TWLadyGrey … 2021-02-03 22:42:33 4047781366 13570… 13570951710417… Twitte…
## 4 "@hueningkey … 2021-02-08 22:10:13 8871101023… 13589… 13588907919350… Twitte…
## 5 "@Kerry_Berry… 2021-01-21 14:38:48 1317176466… 13522… 13519901898837… Twitte…
## 6 "@marva_dale … 2021-01-12 02:53:19 52880685 13488… 13488153531581… Twitte…
## 7 "@Maccabeus24… 2021-05-26 06:57:39 797253074 13974… 13958026036998… Twitte…
## 8 "The dude who… 2021-03-03 18:18:36 1278848505… 13671… 13671776057414… Twitte…
## 9 "@MistyKitty3… 2021-04-19 17:11:38 1377649102… 13841… 13841821901671… Twitte…
## 10 "@ScamBusterr… 2021-05-08 04:39:16 541128677 13908… 13908018110790… Twitte…
## 11 "@lays_barbec… 2021-01-20 23:32:47 3333173253 13520… 13520355616498… Twitte…
## 12 "Does anyone … 2021-05-19 12:44:40 1171401492… 13949… 13949974322038… Twitte…
## 13 "@The_FJC @ca… 2021-03-13 01:20:38 8860623665… 13705… 13703539985286… Twitte…
## 14 "Why the fuck… 2021-05-28 16:13:03 11392232 13983… 13983113667174… Twitte…
## 15 "@peachee_bab… 2021-03-04 03:49:19 1008873383… 13673… 13669397367162… Twitte…
## 16 "@KPIXtv Well… 2021-01-21 04:23:49 7842290130… 13521… 13521058974143… Twitte…
## 17 "@adegbenro61… 2021-03-31 15:38:08 9773862989… 13772… 13772810020375… Twitte…
## 18 "I want one s… 2021-05-01 15:46:19 198029120 13885… 13885201669721… Twitte…
## 19 "@ShellyMKFis… 2021-04-30 03:09:36 1359966370… 13879… 13872507488832… Twitte…
## 20 "See the map … 2021-05-13 09:50:05 1347675931… 13927… 13927791626586… Twitte…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Your Turn ⤵
Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:
ngss_tweets %>%
sample_n(20) %>%
relocate(text)## # A tibble: 20 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <chr> <chr> <chr> <chr>
## 1 "@vhopekenned… 2021-01-26 21:13:33 1292896816… 13541… 13541694293372… Twitte…
## 2 "@ngss_offici… 2021-04-24 09:01:22 9422074612… 13858… 13858787038470… Twitte…
## 3 "Snag this FR… 2021-01-29 02:04:05 1329148492… 13549… 13549735591148… Buffer
## 4 "A3. We find … 2021-05-07 01:21:17 184649645 13904… 13904767978258… TweetD…
## 5 "An #NGSSchat… 2021-01-22 13:10:13 59850402 13526… 13526044833987… Twitte…
## 6 "Now time for… 2021-01-28 22:34:22 29886784 13549… 13549207831522… Twitte…
## 7 "Absolutely l… 2021-04-09 00:22:42 1623116820 13803… 13803151963049… TweetD…
## 8 "I had the pl… 2021-04-07 12:15:35 1322355390… 13797… 13797698228258… Twitte…
## 9 "I continue t… 2021-03-15 23:43:37 8288023367… 13716… 13716080530202… Twitte…
## 10 "@iowaphenome… 2021-04-02 01:31:04 1346650583… 13777… 13777950140681… TweetD…
## 11 "@airmelia @_… 2021-01-05 19:05:50 2198414407 13465… 13461252663393… Twitte…
## 12 "@TedWillard2… 2021-05-21 01:32:28 7000400363… 13955… 13955506606161… Twitte…
## 13 "@KRScienceLa… 2021-02-02 21:32:30 7438955795… 13567… 13566945160199… Twitte…
## 14 "@NGSS_tweeps… 2021-01-31 20:14:45 333727648 13559… 13559707887472… Twitte…
## 15 "@tjscience @… 2021-04-05 11:37:33 1167108745… 13790… 13790346752310… Twitte…
## 16 "Am looking t… 2021-01-01 13:24:28 135128471 13449… 13449979250768… Twitte…
## 17 "Google Earth… 2021-04-15 15:14:57 20657343 13827… 13827140673332… Twitte…
## 18 "Thank you al… 2021-03-19 01:42:51 1449382200 13727… 13727252198493… TweetD…
## 19 "@magnitudeio… 2021-03-08 22:15:48 13040 13690… 13690492361264… Twitte…
## 20 "Welcome to #… 2021-04-02 01:00:00 1346650583… 13777… 13777878682449… TweetD…
## # … with 2 more variables: possibly_sensitive <lgl>, in_reply_to_user_id <chr>Still of the same opinion?
- Yes, these appear much less negative.
What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.
What questions do you have about these data sets? What are you still curious about?
2b. Restructure Data
Subset Tweets
As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we’ll use.
Let’s start with the CCSS tweets first. And since this is a family friendly case study, let’s use the filter() function introduced in previous labs to filter out rows containing “possibly sensitive” language:
ccss_tweets_1 <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE")Now let’s use the select() function to select the following columns from our new ss_tweets_clean data frame:
textcontaining the tweet which is our primary data source of interestauthor_idof the user who created the tweetcreated_attimestamp for examining changes in sentiment over timeconversation_idfor examining sentiment by conversationsidfor the unique reference id for each tweet and useful for counts
ccss_tweets_2 <- ccss_tweets_1 %>%
select(text,
author_id,
created_at,
conversation_id,
id)Your Turn ⤵
Note: The select() function will also reorder your columns based on the order in which you list them.
Use the code chunk below to reorder the columns to your liking and assign to ccss_tweets_3:
ccss_tweets_3 <- ccss_tweets_1 %>%
select(id,
text,
author_id,
created_at,
conversation_id)Add & Relocate Columns
Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.
We’ll use the mutate() function to create a new variable called standards to label each tweets as “ngss”:
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")
colnames(ccss_tweets_4)## [1] "text" "author_id" "created_at" "conversation_id"
## [5] "id" "standards"And just because it bothers me, I’m going to use the relocate() function to move the standards column to the first position so I can quickly see which standards the tweet is from:
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Again, we could also have used the select() function to reorder columns like so:
ccss_tweets_5 <- ccss_tweets_4 %>%
select(standards,
text,
author_id,
created_at,
conversation_id,
id)
colnames(ccss_tweets_5)## [1] "standards" "text" "author_id" "created_at"
## [5] "conversation_id" "id"Before moving on to the CCSS standards, let’s use the %>% operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ccss") %>%
relocate(standards)
head(ccss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Your Turn ⤵
Recall from section 1b. Define Questions that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.
Create an new ngss_tweets_clean data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.
ngss_tweets_clean <- ngss_tweets %>%
filter(possibly_sensitive == "FALSE") %>%
select(text, author_id, created_at, conversation_id, id) %>%
mutate(standards = "ngss") %>%
relocate(standards)
head(ngss_tweets_clean)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss "Please help… 3279907796 2021-01-06 00:50:49 13466201998945… 13466…
## 2 ngss "What lab ma… 1010324664… 2021-01-06 00:45:32 13466188701325… 13466…
## 3 ngss "I recently … 61829645 2021-01-06 00:39:37 13466173820858… 13466…
## 4 ngss "I'm thrille… 461653415 2021-01-06 00:30:13 13466150172071… 13466…
## 5 ngss "PLS RT. Exc… 22293234 2021-01-06 00:15:05 13466112069671… 13466…
## 6 ngss "Inspired by… 3317960226 2021-01-06 00:00:00 13466074140999… 13466…Merge Data Frames
Finally, let’s combine our CCSS and NGSS tweets into a single data frame by using the union() function from dplyr and simply supplying the data frames that you want to combine as arguments:
ss_tweets <- union(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsNote that when creating a “union” like this (i.e. stacking one data frame on top of another), you should have the same number of columns in each data frame and they should be in the exact same order.
Alternatively, we could have used the bind_rows() function from {dplyr} as well:
ss_tweets <- bind_rows(ccss_tweets_clean,
ngss_tweets_clean)
ss_tweets## # A tibble: 35,233 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 124959489… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 887707054… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 124959489… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 127601732… 2021-01-02 00:18:38 13451625486818… 13451…
## 7 ccss "@LisaS4680 … 922132923… 2021-01-02 00:16:11 13451595466087… 13451…
## 8 ccss "@JerryGl291… 122016089… 2021-01-02 00:10:29 13447179758914… 13451…
## 9 ccss "@JBatNC304 … 880914489… 2021-01-02 00:09:15 13447403608625… 13451…
## 10 ccss "@chiefaugur… 124959489… 2021-01-01 23:54:38 13451533915976… 13451…
## # … with 35,223 more rowsThe distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.
However, since both functions returned the same number of rows, it’s clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they intersect(), or where they have duplicate rows.
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
ngss_tweets_clean)Your Turn ⤵
Finally, let’s take a quick look at both the head() and the tail() of this new ss_tweets data frame to make sure it contains both “ngss” and “ccss” standards:
head(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ccss "@catturd2 H… 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 ccss "@homebrew15… 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 ccss "@ClayTravis… 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 ccss "@KarenGunby… 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 ccss "@keith3048 … 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 ccss "Probably co… 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…tail(ss_tweets)## # A tibble: 6 × 6
## standards text author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 ngss @BK3DSci Bria… 558971700 2021-05-21 01:10:28 13955471161272… 13955…
## 2 ngss A1 My studen… 1449382200 2021-05-21 01:10:20 13955474728990… 13955…
## 3 ngss A1: It is an … 136014942 2021-05-21 01:09:58 13955473807585… 13955…
## 4 ngss @MsB_Reilly M… 3164721571 2021-05-21 01:09:54 13955471085775… 13955…
## 5 ngss A1.5 I also l… 14449947 2021-05-21 01:09:46 13955473306029… 13955…
## 6 ngss @MsB_Reilly W… 558971700 2021-05-21 01:09:44 13955471085775… 13955…2c. Tidy Text
Text data by it’s very nature is ESPECIALLY untidy and is sometimes referred to as “unstructured” data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.
Tokenize Text
In Chapter 1 of Text Mining with R, Silge & Robinson (2017) define the tidy text format as a table with one-token-per-row, and explain that:
A token is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.
This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.
For this part of our workflow, our goal is to transform our ss_tweets data from this:
head(relocate(ss_tweets, text))## # A tibble: 6 × 6
## text standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 "@catturd2 H… ccss 1609854356 2021-01-02 00:49:28 13451697062071… 13451…
## 2 "@homebrew15… ccss 1249594897… 2021-01-02 00:40:05 13451533915976… 13451…
## 3 "@ClayTravis… ccss 8877070540… 2021-01-02 00:32:46 13450258639942… 13451…
## 4 "@KarenGunby… ccss 1249594897… 2021-01-02 00:24:01 13451533915976… 13451…
## 5 "@keith3048 … ccss 1252747591 2021-01-02 00:23:42 13451533915976… 13451…
## 6 "Probably co… ccss 1276017320… 2021-01-02 00:18:38 13451625486818… 13451…Into a “tidy text” one-token-per-row format that looks like this:
tidy_tweets <- ss_tweets %>%
unnest_tokens(output = word,
input = text) %>%
relocate(word)
head(tidy_tweets)## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…If you take ECI 588: Text Mining in Education, you’ll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.
As demonstrated above, the tidytext package provides the incredibly powerful unnest_tokens() function to tokenize text (including tweets!) and convert them to a one-token-per-row format.
Let’s tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:
ss_tokens <- unnest_tokens(ss_tweets,
output = word,
input = text)
head(relocate(ss_tokens, word))## # A tibble: 6 × 6
## word standards author_id created_at conversation_id id
## <chr> <chr> <chr> <dttm> <chr> <chr>
## 1 catturd2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 2 hmmmm ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 3 common ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 4 core ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 5 math ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…
## 6 now ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 134517031…There is A LOT to unpack with this function:
- First notice that
unnest_tokens()expects a data frame as the first argument, followed by two column names. - The next argument is an output column name that doesn’t currently exist but will be created as the text is “unnested” into it,
wordin this case). - This is followed by the input column that the text comes from, which we uncreatively named
text. - By default, a token is an individual word or unigram.
- Other columns, such as
author_idandcreated_at, are retained. - All punctuation has been removed.
- Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the
to_lower = FALSEargument to turn off if desired).
Note: Since {tidytext} follows tidy data principles, we also could have used the %>% operator to pass our data frame to the unnest_tokens() function like so:
ss_tokens <- ss_tweets %>%
unnest_tokens(output = word,
input = text)
ss_tokens## # A tibble: 911,173 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… cattur…
## 2 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… common
## 4 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… core
## 5 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… math
## 6 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… now
## 7 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… makes
## 8 ccss 1609854356 2021-01-02 00:49:28 134516970620710… 13451703… sense
## 9 ccss 12495948971… 2021-01-02 00:40:05 134515339159767… 13451679… homebr…
## 10 ccss 12495948971… 2021-01-02 00:40:05 134515339159767… 13451679… i
## # … with 911,163 more rowsYour Turn ⤵
The unnest_tokens() function also has a specialized “tweets” tokenizer in the tokens = argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the @ symbol as illustrated by our @catturd2 friend who featured prominently in our the first CCSS tweet.
Rewrite the code above (you can check answer below) to include the token argument set to “tweets,” assign to ss_tokens_1, and answer the questions that follow:
ss_tokens_1 <- unnest_tokens(ss_tweets,
output = word,
input = text,
token = "tweets")## Using `to_lower = TRUE` with `token = 'tweets'` may not preserve URLs.head(ss_tokens_1)## # A tibble: 6 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… @catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… common
## 4 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… core
## 5 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… math
## 6 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… nowHow many observations were our original
ss_tweetsdata frame?How many observations are there now? Why the difference?
Before we move any further let’s take a quick look at the most common word in our two datasets. To do so, we’ll introduce the easy to use count() function from the {dplyr} package.
Like most functions we’ve introduced, the first argument count() expects is a data frame which we provided with the %>% operator, followed but the column, in our case word, whose values we want to count:
ss_tokens_1 %>%
count(word, sort = TRUE)## # A tibble: 74,235 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 the 25818
## 4 to 20478
## 5 and 15552
## 6 of 13106
## 7 a 12472
## 8 math 11788
## 9 is 11562
## 10 in 10076
## # … with 74,225 more rowsWell, many of these tweets are clearly about the CCSS and math at least, but beyond that it’s a bit hard to tell because there are so many “stop words” like “the,” “to,” “and,” “in” that don’t carry much meaning by themselves.
Remove Stop Words
Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The stop_words dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or filter() to only use one set of stop words if that is more appropriate for a certain analysis.
Let’s take a closer the lexicons and stop words included in each:
View(stop_words)The anti_join Function
In order to remove these stop words, we will use a function called anti_join() that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:
For a good overview of the different dplyr joins see here: https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119.
Now let’s remove stop words that don’t help us learn much about what people are saying about the state standards.
ss_tokens_2 <- anti_join(ss_tokens_1,
stop_words,
by = "word")
head(ss_tokens_2)## # A tibble: 6 × 6
## standards author_id created_at conversation_id id word
## <chr> <chr> <dttm> <chr> <chr> <chr>
## 1 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… @catt…
## 2 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… hmmmm
## 3 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… common
## 4 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… core
## 5 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… math
## 6 ccss 1609854356 2021-01-02 00:49:28 1345169706207109… 13451703111… makesNotice that we’ve specified the by = argument to look for matching words in the word column for both data sets and remove any rows from the tweet_tokens dataset that match the stop_words dataset. Remember when we first tokenized our dataset I conveniently chose output = word as the column name because it matches the column name word in the stop_words dataset contained in the tidytext package. This makes our call to anti_join()simpler because anti_join() knows to look for the column named word in each dataset. However this wasn’t really necessary since word is the only matching column name in both datasets and it would have matched those columns by default.
Your Turn ⤵
Use the code chunk below to take a quick count of the most common tokens in our ss_tweets_2 data frame to see if the results are a little more meaningful, then answer the questions that follow.
ss_tokens_2 %>%
count(word, sort = TRUE)## # A tibble: 73,596 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 amp 2904
## 6 #ngss 2655
## 7 students 2559
## 8 science 2300
## 9 standards 2273
## 10 education 2174
## # … with 73,586 more rowsYour Turn ⤵
How many unique tokens are in our data tidied text?
- 73,596
How many times does the word “math” occur in our set of tweets?
- 11,788
Custom Stop Words
Notice that the nonsense word “amp” is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don’t carry much meaning but skew our data by being so prominent.
Let’s create a custom stop word list by using the simple c() function to combine our words. We can the add a filter to keep rows where words in our word column do NOT ! match words %in% my_stopwords list:
my_stopwords <- c("amp", "=", "+")
ss_tokens_3 <-
ss_tokens_2 %>%
filter(!word %in% my_stopwords)Let’s take a look at our top words again and see if that did the trick:
ss_tokens_3 %>%
count(word, sort = TRUE)## # A tibble: 73,593 × 2
## word n
## <chr> <int>
## 1 common 26665
## 2 core 26470
## 3 math 11788
## 4 #ngsschat 3059
## 5 #ngss 2655
## 6 students 2559
## 7 science 2300
## 8 standards 2273
## 9 education 2174
## 10 school 2154
## # … with 73,583 more rowsMuch better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.
Before we move any further, let’s save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:
ss_tidy_tweets <- ss_tokens_3
write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))3. EXPLORE
Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we’re going to keep things super simple and focus on:
Top Tokens. Since once of our goals is to compare tweets about the NGSS and CSSS standards, we’ll take a look at the to 50 words that appear in each.
Word Clouds. To help illustrate the relative frequency each of these top 50 words occurs, we’ll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.
3a. Top Tokens
First, let’s take advantage of the the %>% operator combine some of the functions we’ve used above with the top_n() function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.
Let’s take a look at the top tokens among the CCSS tweets by filtering our standards by CCSS, counting the number of times each word occurs, and taking the look at the 50 most common words:
ccss_top_tokens <- ss_tidy_tweets %>%
filter(standards == "ccss") %>%
count(word, sort = TRUE) %>%
top_n(50)## Selecting by nccss_top_tokens## # A tibble: 50 × 2
## word n
## <chr> <int>
## 1 common 26599
## 2 core 26405
## 3 math 11688
## 4 education 1917
## 5 kids 1821
## 6 standards 1810
## 7 school 1806
## 8 dont 1622
## 9 grade 1443
## 10 people 1410
## # … with 40 more rowsNot surprisingly, our search terms appear in the top 50 but the word “math” also features prominently among CCSS tweets!
Word Clouds
Word clouds are much maligned and sometimes referred to as the “pie charts of text analysis,” but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.
The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the wordcloud2() function, it will look for a word column and a column with frequencies or counts, i.e., our column n that we created with the count() function.
Let’s run the wordcloud2() function on our ccss_top_tokens data frame.
wordcloud2(ccss_top_tokens)As you can see, “math” is a pretty common topic when discussing the common core on twitter but words like “core” and “common” – which you can see better if you click the “show in a new window” button or run the code in you console – are not very helpful since those were in our search terms when pulling data from Twitter.
In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.
ccss_top_tokens %>%
filter(word != "common" & word != "core") %>%
wordcloud2()Your Turn ⤵
In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!
ss_tidy_tweets %>%
filter(standards == "ngss") %>%
count(word, sort = TRUE) %>%
top_n(50) %>%
wordcloud2()## Selecting by nAlso, take a look at the help file for wordclouds2 to see if there might be other ways you could improve the aesthetics of this visualization.
4. MODEL
Now that we have our tweets nice and tidy, we’re almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the tidytext and dplyr packages respectively:
How do you “measure” sentiment?
Sentiment analysis tries to evaluate words for their emotional association. In Text Mining with R: A Tidy Approach, Silge aand Robinson point out that,
One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.
This isn’t the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you’ll find that is it often-used in publications that utilize sentiment analysis.
The {tidytext} package provides access to several sentiment lexicons, sometimes referred to as dictionaries, based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.
The three general-purpose lexicons we’ll focus on are:
AFINNassigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.bingcategorizes words in a binary fashion into positive and negative categories.nrccategorizes words in a binary fashion (“yes”/“no”) into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.
Note that if this is your first time using the AFINN and NRC lexicons, you may prompted to download both Respond yes to the prompt by entering “1” and the NRC and AFINN lexicons will download. You’ll only have to do this the first time you use the NRC lexicon.
Let’s take a quick look at each of these lexicons using the get_sentiments() function and assign them to their respective names for later use:
afinn <- get_sentiments("afinn")
afinn## # A tibble: 2,477 × 2
## word value
## <chr> <dbl>
## 1 abandon -2
## 2 abandoned -2
## 3 abandons -2
## 4 abducted -2
## 5 abduction -2
## 6 abductions -2
## 7 abhor -3
## 8 abhorred -3
## 9 abhorrent -3
## 10 abhors -3
## # … with 2,467 more rowsbing <- get_sentiments("bing")
bing## # A tibble: 6,786 × 2
## word sentiment
## <chr> <chr>
## 1 2-faces negative
## 2 abnormal negative
## 3 abolish negative
## 4 abominable negative
## 5 abominably negative
## 6 abominate negative
## 7 abomination negative
## 8 abort negative
## 9 aborted negative
## 10 aborts negative
## # … with 6,776 more rowsnrc <- get_sentiments("nrc")
nrc## # A tibble: 13,901 × 2
## word sentiment
## <chr> <chr>
## 1 abacus trust
## 2 abandon fear
## 3 abandon negative
## 4 abandon sadness
## 5 abandoned anger
## 6 abandoned fear
## 7 abandoned negative
## 8 abandoned sadness
## 9 abandonment anger
## 10 abandonment fear
## # … with 13,891 more rowsAnd just out of curiosity, let’s take a look at the loughran lexicon as well:
loughran <- get_sentiments("loughran")
loughran## # A tibble: 4,150 × 2
## word sentiment
## <chr> <chr>
## 1 abandon negative
## 2 abandoned negative
## 3 abandoning negative
## 4 abandonment negative
## 5 abandonments negative
## 6 abandons negative
## 7 abdicated negative
## 8 abdicates negative
## 9 abdicating negative
## 10 abdication negative
## # … with 4,140 more rows✅ Comprehension Check
How were these sentiment lexicons put together and validated? Hint: take a look at Chapter 2 from Text Mining with R.
Why should we be cautious when using and interpreting them?
Come to the Dark Side
As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.
The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.
One drawback to VADER is that it can take a little while to run since it’s computationally intensive. Instead of analyzing tens of thousands of tweets, let’s read in our original ccss-tweets.csv and take instead just a sample of 500 “untidu” CCSS tweets using the sample_n() function:
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
sample_n(500)## Warning: One or more parsing issues, see `problems()` for details## Rows: 27230 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.ccss_sample## # A tibble: 500 × 8
## text created_at author_id id conversation_id source
## <chr> <dttm> <dbl> <dbl> <dbl> <chr>
## 1 "@resawynd @rv… 2021-04-05 01:47:17 1.75e 8 1.38e18 1.38e18 Twitte…
## 2 "Joe must be u… 2021-01-15 02:51:07 3.78e 7 1.35e18 1.35e18 Twitte…
## 3 "@thee_snek No… 2021-04-01 03:04:58 1.33e18 1.38e18 1.38e18 Twitte…
## 4 "Please share … 2021-03-08 22:29:58 3.32e 9 1.37e18 1.37e18 Twitte…
## 5 "@effinghadit … 2021-01-01 03:59:06 3.85e 8 1.34e18 1.34e18 Twitte…
## 6 "Is this the c… 2021-01-14 22:36:13 1.28e18 1.35e18 1.35e18 Twitte…
## 7 "Read Free Com… 2021-04-29 12:53:39 1.39e18 1.39e18 1.39e18 Twitte…
## 8 "Somehow this … 2021-01-15 04:21:41 1.20e18 1.35e18 1.35e18 Twitte…
## 9 "@moorela66180… 2021-01-19 09:46:43 1.35e18 1.35e18 1.35e18 Twitte…
## 10 "@revlaurelj I… 2021-02-07 14:30:54 1.05e18 1.36e18 1.36e18 Twitte…
## # … with 490 more rows, and 2 more variables: possibly_sensitive <lgl>,
## # in_reply_to_user_id <dbl>Note above that we passed our read_csv() output directly to our sample() function rather than saving a new data frame object, passing that to sample_n(), and saving as another data frame object. The power of the %>% pipe!
On to the Dark Side. The {vader} package basically has just one function, vader_df() that does one thing and expects just one column from one frame. He’s very single minded! Let’s give VADER our ccss_sample data frame and include the $ operator to include only the text column containing our tweets.
vader_ccss <- vader_df(ccss_sample$text)
vader_ccss## text
## 1 @resawynd @rvnwnd The real conspiracy is rejecting standards like Common Core based on GOV mandates & classwork without fact checking its requirements (standards). \nNeed to discuss motives & goals & how classwork can support them & how everyone can access those solutions like 50 years ago!
## 2 Joe must be using common core math again😂 https://t.co/Ycrt3cCo3G
## 3 @thee_snek No they named common core, therefore the recession and 9/11 weren’t as bad
## 4 Please share this with PARENTS as well as teachers. Teachers watched today's test-based craziness unfolding. Parents may still be hazy on how standardized tests and curricula are wrecking education for many lower readers in poor districts. https://t.co/al93dWoaZC
## 5 @effinghadit @scholarlymama @MattBinder I realize your comprehension of complex events is retarded by a probable lifetime of blaming others for the negatives in your life and the Horrors of Common Core Math... But.. You have been SCAMMED, MANIPULATED and LIED to SO OFTEN..you don't even QUESTION ? I pity you... https://t.co/krJfhj2Uhj
## 6 Is this the common core biology classes? 🧬 https://t.co/hiPdoUwVXD
## 7 Read Free Common Core English Workbook: Grade 5 English => https://t.co/IQc5cFTcal
## 8 Somehow this whole stimulus debacle is the fault of common core math, I’m sure of it.
## 9 @moorela66180327 @GrandTeton41 @usa1forme Probably promotes common core
## 10 @revlaurelj I am set in my ways.\n\nTwo spaces for me.\n\nCommon Core math is awful. The old way was better.\n\nPluto is still a planet.
## 11 @TopCat4647 Note... Biology, Physical Education etc became "sciences" when greed,money&power hungry psychopaths grouped the "Studies" together under the word n corrupted the world via a "common core education" in state ran schools\n\nRule 6 https://t.co/JkSSmnvACg
## 12 Download Kindle Common Core English Workbook: Grade 7 English -> https://t.co/rgQj5XqM5P\n\nCommon Core English Workbook: Grade 7 English Ace Academic Publishing pdf download\nCommon Core English Workbook: Grade 7 English Ace Academic Pub
## 13 It’s the year 2030. \n\nWe work for GameStop Charter. \n\nThey pay us in $Doge. \n\nPresident Roaring Kitty replaces Common Core w/ Tendie Tiers.
## 14 @trumwill Long term make statistics and causal inference a part of common core 😊 (says an instructor of statistics)
## 15 @HunterM00956147 @lauren_price_ @MclovinTc @FogCityMidge Not with common core math, answer is not 2 anymore. We are not wearing masks in Southeast Tx, and doing great. Breathe in that bacteria and fungus from mask wearing like those here in ICU we are caring for. No Covid in ICU.
## 16 @MoniseLSeward And these are the same schools that they spent months posting foolishness over common core math on FB with their aunts and neighbors and how schools were indoctrinating their kids. 🙄
## 17 @Davidtmp @curtis08467059 @AZGOP @kelliwardaz When someone gets more votes, they win. That’s not common core. That’s common sense. Biden got more electoral votes therefore he won. Get over it, Trump lost.
## 18 @Elenaforever13 @AMJ_View Gov DeSantis is also ending the teaching of Critical Race Theory indoctrination in FL schools & ended Obama’s confusing Common Core Studies.
## 19 @elvaque46965330 @GeorgePapa19 Still more believable then 85 million people voting for that potato Biden.\n\nBtw even using common core math I cant make 84 and 74 million equal 133 million registered voters. https://t.co/rjqiTb11Dm
## 20 @Cosbybill6 @Jeff75998275 @MinarchistR @dwightlwolfe @laurenboebert Really??? Do you live under a rock? Common core is one example - a curriculum required in every state. Utilities and farmers - Obama put so many ridiculous regulations with severe fines for noncompliance. It’s increased the cost of food and basic utilities for everyone.
## 21 @Antman0528 Looks like Common Core Sex Education to me 😂
## 22 @NCHouseSpeaker @monicaonairtalk Good. Get rid of common core next !!!!
## 23 More funding doesn't solve the issue in itself. Government schools here in Kentucky have DOUBLE the money that the typical private school does, and the private schools still outperform academically overall.\n\nBut either way, I'm not particularly a fan of common core. https://t.co/9pqxPTEu7S
## 24 Common Core Math or was cheating involved? https://t.co/PpZdfCJ1vd
## 25 29/ Of the 3 curricula I have been using for comparison, the Common Core is the most vague about how it incorporates percent; it is not introduced until Grade 6, however, and incorporates ratios and proportional relationships, allowing connection to fractions and equivalency.
## 26 Clint Richardson - Common Core, Agenda 21, And Global Privatization - YouTube https://t.co/cG0NfGXKC2
## 27 @CityPretzelDC @SophietheBear1 @BeakFinch One of my fave Common Core examples: https://t.co/FdD7frAHro
## 28 @KevinMKruse Is this that new common core testing? I'm a reasonably educated person, and that is nothing more than meant to be entirely impossible to get through.
## 29 Doesn't remember the test content being relevant or stimulating; in Middle School, she returned to a neighborhood school-- remembers a clear shift test-centric focus from pre-common core to post-common core curriculum
## 30 @ariellebc90 @NewEmergingKing I think some don't want to and some would like to but might fear "turning off" their particular audience so to speak. The sides need to focus more on the common core cause. And yes, all factions have those polarizing types. Definitely would like to see those types subside.
## 31 @RepubaD @LonieRosekelly @BenjaminEW @SenTedCruz @tedcruz @HoustonChron Yeah but you liberals was raised on common core math...We Won and y’all love communism!🇺🇸
## 32 @JKash000 @DigitalPatriot0 Simple math! NOT common-core! \n\nI like it. 👍👍👍
## 33 @BlackJael2087 @FoxNews @NYGovCuomo Common core is math. Your statement is a dumpster fire.
## 34 @sweetdaddy1963 @chelseahandler 90/20, is that common core math?
## 35 Read it on PsychToday - Fear of Flying's Common Core https://t.co/jFa2g6FsDx https://t.co/xIFZPJ7Bu6
## 36 i’ve decided that undertale is unironically a work of modern art and should be held to the same standard as shakespeare’s hamlet in future common core
## 37 @RioSlade Did she use common core or something
## 38 Common core is bad
## 39 @NealMcCluskey's right. May we learn at least one lesson from the Common Core fiasco--which, by the way, is ongoing? https://t.co/liJASQLGxr
## 40 @missy_coulter @OceanEyes50 Is that between 4 and 5? Not sure without my common core charts in front of me.
## 41 @openpdxschools @NYC_stateofmind @angrybklynmom As a former middle school math teacher this sadly comes as no surprise. Dumbing down of Amer schools started with Bush & cont. with Common Core. No rote memorization allowed, not understanding the brain eventually catches up with the info. Parents should be terrified & pissed.
## 42 @matt_kohrs In common core
## 43 “The rise of reforms like Common Core and high stakes testing regimens have meant a loss of professional autonomy for teachers.” #txed #txlege @SenLarryTaylor https://t.co/5tTITZkmmd
## 44 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 45 @JoeSilverman7 Common core grad.
## 46 @the_jon_a_thon @Frippin2 Common core math.
## 47 Check out our #ReadyToUse emergency sub plans! \n\nEach theme based unit covers all the main subject areas and includes lesson objective, common core standards, #printable student handouts and key learning points.\n\nhttps://t.co/n7Wj0HBZ7Q https://t.co/1PCUaQWfMd
## 48 @Hupperware @jprutherford Is that common core? #math
## 49 @peaceluvgators Ask NCTM (National Council of Teachers of Math) and other countries.\nhttps://t.co/6bZ5GQ4JY3\nhttps://t.co/kPEvtV1mPR
## 50 @chrismanfrank @jasonfx we know common core is beyond bankrupt. next step is creative destruction of the entire system
## 51 ● [Read] EPUB ●\n\n▶ https://t.co/fFfVOPePGp ◀\n.\n.\nTitle >> 4th Grade Common Core Math: Daily Practice Workbook - Part II: Free Response | 1000+ Practice Questions and Video Explanations | Argo Brothers\nRating => 99 out of 100 base
## 52 @catturd2 Common core will be the death of the generation subjected to it.
## 53 @BuzzPatterson Check into Obama’s Common Core (aka “Common Bore”) book “deals “.
## 54 Download this FREE activity to introduce children to the concept of cause and effect! ▶️ https://t.co/Q95cX9YC3L\n\nCommon Core alignment: CCSS.ELA-LITERACY.RI.K.3\n\n#teachertwitter #primaryschool #kindergarten https://t.co/hnb0SVNFui
## 55 @MsUribe4th You're going to give the "common core" phobics an aneurysm! :D
## 56 @RebelNewsOnline More of his common core BS.
## 57 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/W3YWh462d7\n@MAXMUSICK https://t.co/rfceLny2OS
## 58 @toddlerr Common core was a joke!
## 59 @ProfDuff this is the common core of cheesesteak length records.
## 60 @comradespice @dontlickchalk Our common core is love. We can all learn & realize that things are not that different. Kindness is the key to everyone's heart. Please use proper judgement. Ha. Good luck.
## 61 @NOPatriotGames1 🚨Common core math🚨\nIt all makes sense now! 😠🤬
## 62 @Boojahideen Common Core math grad.
## 63 @Kredo0 Biden admin + Price gouging =\n\nTheir common core.
## 64 @ZeeLeafs What exactly is common core? Is that a music genre?
## 65 @SamuelWBell Common core isn't perfect, but tweeting out of context images isn't helpful. I'm willing to trust that this educator is a professional and knows what they are doing how to implement it in their classroom.
## 66 3 penalties on Buffalo and 1 on KC but they all offset? Is this common core math? #BUFvsKC
## 67 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 68 @arneduncan Common Core is a disaster
## 69 >> 4th Grade Common Core Math: Daily Practice Workbook - Part II: Free Response | 1000+ Practice Questions and Video Explanations | Argo Brothers <<\n✔ Click Link Here //> https://t.co/CpX56j5sm8 4th Grade Common Core Math: Daily Prac
## 70 @ForzaCorrado Common core math grads, I see 🤡 🤪
## 71 @TeachToAmerica @Missy76631003 @scotfasco @DanScavino Hey teacher boy. Explain how that is idiotically not true. Please spell out the math. No common core - real math. So you obviously do not understand basic economics. That’s what’s wrong with you liberals.. no sense of basic economics.
## 72 @BullTarheel @dwright100 There math is as great as common core math. Lol Semper Fi
## 73 It's that common core crap.\n\nGood luck, you're likely to never understand https://t.co/xTrLL5gd5i
## 74 yo, to who ever created common core, ur getting a 12 gauge to the face
## 75 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 76 @AdamLefkoe This is what happens when kids learn math through common core 🤦🏻♂️
## 77 Just the other day I was wondering what ever happened to Common Core and Right's attack on 'critical thinking'.\n....Found it! \n(Because they know if we teach young ppl to think critically, the GOP is doomed) https://t.co/l1d0CgMYT8
## 78 @_mexodus_ Girl took common core match....🤣 All those extra steps
## 79 DEFCON 3 [HARD CUT] Unusual Cloud activity: Stargate Command adopting Common Core at latest by this quarter.
## 80 1.common core \n2.teacher unions \n3. No school choice \n#SchoolChoice #schools #prageru https://t.co/0FzXuiXe2x
## 81 Make me Secretary of Education and this will be your new common core. https://t.co/mmqY2dQAGN
## 82 @QTRResearch Give him a break he was use Common Core Math 🧮
## 83 @TABYTCHI ...love that Common Core math!
## 84 @EROTHCJ5 Common core math...
## 85 @RobertLusetich @megynkelly Maybe because he's a liberal douche oh and let's not forget common core. Worse thing ever developed he needs to stay out of shit he doesn't know about it. Go build another computer program, don't try to run the world
## 86 Common Core is a part of Maine law even though those words don't appear in statutes. https://t.co/snxaTCSRya #MEpolitics
## 87 @SarahBurssty we started common core when I was in the first grade i think, idk I don't remember anything past last year bc of how much i hate school
## 88 @parlertakes damned common core math!
## 89 @oldschoolquimby American common core curriculum unfortunately does not require critical thinking as a diploma requirement.
## 90 i play jumpstart, but math's hard\nand i can't stand learning anything\n i like when grown-ups call me smart\nbut i'm dumb, so i won't add sums up anymore\nand i barely even care that i'm forgetting all the common core
## 91 @mariana057 So I’m confused...is the answer really no it’s not reasonably 75? If so this is the “alternative facts” explanation and clearly common core math is a Republican conspiracy to make our youth complete idiots.
## 92 Check your state's standards when you arm for battle! For example by Common Core, knowing the formula for the area of a trapezoid is not an expectation, but what your kid did -- understand area as additive, decompose into known shapes -- is. https://t.co/CyEn4g5hCD
## 93 @JeannieG40 Nothing. Nothing is the narrative. All is good. Checking the numbers using common core mathematics.
## 94 oh yeah well if the senate convicts trump the next time the GOP gains power they will impeach and convict \n\n*Spins wheel*\n\nEric Holder for\n\n*Spins wheel*\n\nimplementing Common Core on behalf of\n\n*Spins weighted carnival wheel that always lands on the same thing*\n\nGeorge Soros
## 95 @ILGOP Please stop embarrassing your fellow white people with this ignorant, racist, garbage. Common core has nothing to do with race. Children are not being indoctrinated. The white race is not being extinguished. No wonder to GOP is dying.
## 96 Common core math? https://t.co/fcN5UaoVTw
## 97 @TellMeNow_ Must be a product of public school common core education
## 98 In the next two days Trump will unveil his new health plan, build the wall, roll out infrastructure week, grow the economy by 4%, investigate Hillary, eliminate common core and replace this country’s failing shower heads and toilets.
## 99 All penalties offset ahahahahah refs doing some common core math over here where 3=1 Jesus Christ almighty
## 100 @miles_commodore Common core math 🤷♂️
## 101 @RufusKings1776 Yep you are doing common core math. Terrimacachut. Chutiya
## 102 LRT When I was in high school doing any math made me wanna sewer slide let alone any kind of common core
## 103 Top 10 Reasons Why Standardized Testing Is Here to Stay https://t.co/5cSYQragyv #commoncore
## 104 ⭐️ Understanding where a fraction is located on an open vertical number line helps students \n👉divide fractions\n👉develop fraction sense\n👉compare & order fractions\n\n👉Where is the finger pointing on each number line? \n👉What is the same & different?\n👉Common Core #4NF3 #5NF7 https://t.co/CQWAHgR7Ie
## 105 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 106 Meanwhile, I remain agog at this summary of Common Core. How they got here... I have not a clue.\n\n"A few reforms of note have been attempted to improve America’s civic educational system... Common Core appeared to be a promising way..." https://t.co/J0e9Karv97
## 107 It’s me realizing everyone did math the common core way in their head and I was the weird one that didn’t
## 108 @SKYRIDER4538 @SassyGirlD1 @OriginalGoalie @RodFair2 @AMErikaNGIRLLL @Sassychickie @vampyanji @PatriotGaGa1 @RuthieRedSox @skb_sara @BackatyouLife76 @WLouT2 Common Core math, it seems totally legit.
## 109 With this👇👇👇👇bullshit, now we know where common core is taking our kids... https://t.co/kSe940i8n0
## 110 @DonaldJTrumpJr That is what Obama’s common core started. Dumb down American kids
## 111 If You Liked Common Core, You're Going To Love Joe Biden's Civics https://t.co/Q7vsNWFVni
## 112 @Msdebra_01 @MarcusT23762392 Another reason I homeschool!! And I teach cursive! And the constitution and the Declaration of Independence...Obama wanted common core for a reason!!
## 113 @andressamgonza1 @fafagirl10 @AndrewClover3 @anghunt26 @mike_leduc @JackieHillPerry This what Common Core learning does to their brain.🤠🤡
## 114 @Dougf1099 @MsAvaArmstrong This, in no small part, is due to the 'education' system children are exposed to...\n- No Child Left Behind 2001\n- Common Core 2010\n- Pretty much all universities\n\n~Twenty Years of EPIC BS force fed to developing children and young adults = Welcome to Now
## 115 That must be Michaels common core math they used!!! https://t.co/1g0EXUgXrS
## 116 Fear of Flying's Common Core. Psychology Today https://t.co/7nMRyt4YuC RT travel #airlines #IARTG #book travel https://t.co/AqV3aCp8Ba
## 117 @brandi_love I like to think that you and I are on the same page on many issues,especially common core math.\n\nI think we both agree it's stupid.
## 118 @MattDiMattesa @Bobby_Bonsai @chrislhayes Your neighborhood needs a neighborhood is basically my argument for schools being very locally managed & funded. The federal government does things like NCLB, national testing standards & Common Core. I don't agree w/ the minimum wage. For me, it means I have to fire an employee
## 119 @cswignes @courtneypertt @_JGESA PPL who complain Common Core should allow any method that gets the correct answer are rejecting lessons that teach new methods.\nCC requires the 'old' methods.
## 120 @windkbba Please refer to common core
## 121 @seanhannity @MyPlace4U How bad is (( #COMMON #CORE #MATH )) by bill gates \n\nEven the govt public school teachers \nDon’t see corruption in this #copol\n\n#PENNSYLVANIA:\nSent out 1.7 million ballots but counted 2.5 million ballots.\nCounted 202,377 more votes than they have voters.\n\n& tax payers money 👇 https://t.co/L3qNeigddh
## 122 @robbystarbuck Common Core & Critical Race Theory are basically part of the plan to destroy America’s Children. An insidious path foretold in the Communist Manifesto. Destroy from within. No need for nukes. The mind is a terrible thing to waste. #CommunismKills
## 123 Please retweet! Oh--and ideally we're talking about something that explicitly corresponds to Common Core. Thanks. https://t.co/JpB3jUBH2W
## 124 @cppsfan @realchrisrufo the GOP moron is the reason for the STUPID generation we have today seeing as how COMMON CORE was introduced by the GOP and lets see the GOP has been DE-FUNDING education since the 60'S and it is getting worse with each passing year so no i would NEVER trust the gop with anything
## 125 @SandraDees21 @Jennife15496769 Common Core Math!
## 126 @laurenboebert Good. Common core is government indoctrination.
## 127 @raymondjlee Common core math is obsessed with creating 10 first and then adding the left over. I’m going through this a second time with the youngest after surviving common core when it was new back when my eldest was 6 🤦🏼♀️
## 128 @RantGuy @MDWIZ1952 @stahawg @ChuckCallesto @SenTomCotton Go back and watch the hearing coverage. Running the same ballots through over and over to tally 80 million does not really add up to 80 million. Get away from that common core math.
## 129 CVC Phonics Word Houses | Consonant, Vowel, Consonant Sounds | Phonics Worksheet | Grades K-1 | No Preparation Packet | Common Core Aligned by Cameron Frank Products for $1.97 https://t.co/apMW8ArKex
## 130 @RealPaulAhnert Economics 101. \nNot common core math 😁😁😁
## 131 @NYScanner Common core math?
## 132 Common core math https://t.co/LCUUGno5t8
## 133 I invented common core math
## 134 AOC 2018: we have 12 yrs left \nAOC 2019: we have 10 yrs left\n\nKerry 2021: we have 9 yrs left\n(Common core math and science)\n\nKerry and AOC need to talk.... \nThe end of the world is not game.
## 135 @BetsyDeVosED So did you ever actually get rid of Common Core? Or did you just take up space for four years?
## 136 @disco_socialist @RealHtownDude So much for Common Core and its coverage of Math.
## 137 @TwittaChicca @LovelyGigi33 @vicksiern People tried to expose Bill Gates and warn us but it just quietly died. I personally don't trust him along with his Common Core and ethnic approach to math. https://t.co/op0tCinMIf
## 138 Does @elonmusk use ethnomathematics, Common Core math or pure mathematics in planning trips to Mars? Just thinking ahead on my vacation planning. 🤔
## 139 K12 has thrown $ at whole lang, phonics, std. testing, common core and God knows what else, while society was being completely transformed by the web. Mention info literacy as a priority, and ppl suggest that maybe social studies teachers could spend a day on source analysis.
## 140 @GlennnDavid I did a long time ago!\nThey teach socialism,communism, anti trust,hate,sadness, and how to lie!!! China and Japan are so deep in our kids schooling look at "common core" it will make you sick!!!\nBeating the drum of communism!\nWhen u day teacher u think liberal don't you?
## 141 Absolutely TRUE - we have GOT to get the "sense of Family" back. It was lost because of the Clinton's "Common Core Curriculum" and other garbage being taught in our public schools and that - THAT is why we are where we are! Anyone, please show us how this is NOT true - please! https://t.co/ZC7N2RFu1g
## 142 @beth_holbrook8 Been under $2 a gallon until after Inauguration, (1.60-1.80) more 2.49! Not 6 months but your math comes common core math
## 143 If You Liked Common Core, You're Going To Love Joe Biden's Anti-American Civics Project https://t.co/IGOhzakOdm
## 144 @SarahConnor39 Awesome example of the common core education "program" dumbing your children down!
## 145 @parlertakes Is this some new Common Core math crap??
## 146 @SenSasse https://t.co/5U93Onghmu\n\nHow is it possible the same 15,000 ballots were\ncounted on Election Night & then re-counted 5 \nadditional times & NOT once in these 6 counts\ndid they have the same Vote total ?\n\nIs this your Common Core Math, you support ?\n\nPLEASE explain this in detail !
## 147 @Shem_Infinite This must be that common core dancing I've heard about
## 148 EPUB Download Grade 5 Test Practice for Common Core (Barron's Core Focus) -> https://t.co/KuxctiakM7
## 149 @BoltsGotNext @POTUS What happens when you teach common core math? You get people who don’t understand 81,283,361 is more than 74,222,960. They can’t even figure out 306 is more than 232. 😂 Especially when Mommy Karen goes in and screams to get their grades changed when they fail.
## 150 @JamaalBowmanNY Not so simple if you were taught using Common Core Math I would imagine
## 151 @BreitbartLondon Common core education has brainwashed the next generations into total submission.
## 152 @jamie2181 Then 9 = common core
## 153 Yeah thats common core math where 2 + 2 = 85 w/ clear election 🙄🤣🤣🤣🤣🤣\n\nThey make it so obvious its laughable \n#fakeNEWS https://t.co/7KpZkbT7zV
## 154 @sososukai \n-sukai and sangyeon\n-gwsn propaganda\n-putting ur keys in the egg carton\n-common core math
## 155 @Lisak52 Yes it’s unreal! Thankfully I pulled my daughter out of public school in fourth grade. Her private school doesn’t have to do common core. I couldn’t even help her because I didn’t understand it myself!
## 156 @rasmansa The people who “misunderstand” CRT are much like those who “misunderstand” common core math. They have no intention of understanding.
## 157 @T_S_P_O_O_K_Y @zestyne3 I never knew there was common core law schools until Z. 😳
## 158 Yessiree Bob\nAmericans have been living better and saving money ever since they witness the last US trade surplus and total US debt was less $550 billion dollars in 1975\nThat common core math shit taught them that\nReally they only a little over $27 trillion dollars in debt now😂
## 159 @AlamoOnTheRise @sfchronicle You must use common core math think... we were billions in debt. The only reason he has extra cash is because he got a federal bailout... it wasn't because the economy bounced back... were still limited open on so much here... until June 🙄
## 160 @GuidoFawkes1976 @darilyn Much better than that common core crap that gave ChinaJoe 17M more votes than voters!
## 161 @alifarhat79 You must’ve graduated from Obama’s Common Core Math 😆 🥴 https://t.co/wttUtgKljk
## 162 I really hope the college common core curriculum changes in the future to teach you more of the things you really need to know in life like taxes, navigating health care, and etc
## 163 @LakeCait @nytimes The fourth thing was Common Core.
## 164 Common core right there https://t.co/0gYHaJm1zM
## 165 Good for you!! We don’t want your Common Core pushing, alternative- fact revisionist kind around our teachers anyway. Teaching is difficult enough without your “my kid is never wrong” bullshit. #SupportOurTeachers https://t.co/kU0eP0touS https://t.co/xXF2KAP3Eg
## 166 @BuzzPatterson @Virgini25217077 @BillFOXLA She can’t do math unless it’s common core math , hence why it won’t add up for her
## 167 @iandprior This should be done across the nation with all school boards that want to initiate critical race theory in their schools. (or same curriculum hidden under another name like done with common core)
## 168 @TheLeoTerrell ATTENTION PARENTS!! The way Leftwing Fascist maintain power is by Indoctrinating Your Children! If you see a Common Core Program in your school, beware. Teachers & Professors r indoctrinating students to hate themselves, be racists, and hate America. How did Dems get so Evil⁉️
## 169 @miles_commodore That’s the new common core math
## 170 @NicholasFerroni Are you venting only for more $'s & bene's or are you venting against teachers' unions' strangleholds over parents choices for their kids, insane Common Core math; SJW/Critical Race Theory indoctrination; White kids forced to denounce their "inherent racism & privilege"?
## 171 @catturd2 Common core education.
## 172 Kindle Free Math Common Core State Standards, Grade 6 (Quick Study: Academic) -> https://t.co/bhRMg1ToOm
## 173 @IM_Communityitw @realhealthtoken There are five common core issues in technology today: scalability, security, interoperability, speed and privacy. How will Health Token tackle these problems
## 174 When President Obama truly regrets. Now, as we work with our politicians, our leaders don’t understand me. End — end Common Core should — it wasn’t until I hear — if possible — a drug problem? American warriors fought through the wonder of the most valuable player award.
## 175 @sofain “Common core” isn’t a method of teaching and I don’t understand why people keep claiming it is and using it as a buzzword for “one teacher taught something in a method I dislike”
## 176 @PNW_working_mom The new way they teach math makes it hard for parents to help with math now too. Ideally most of these gaps should be filled with a full school year next year. They claim that’s one of the advantages of common core math.
## 177 @pjayevans what the heck is that an Illuminati hat????? (I have a hard time reading cursive. thanks common core!)
## 178 @imjdsharp Ah...but that's where common core math comes in.
## 179 @districtai Not part of Common Core although over estimating and under estimating are there LOL
## 180 @r2kmoon2 @HenryMarten1602 @Amy_Siskind Is this common core English?
## 181 @BillGates Maybe this explains why you are so evil. A man who believes in depopulating the Earth should not be selling vaccines, factory made meat, poop water, common core & critical race theory, blocking the sun for “climate change”, etc.
## 182 @MetroCapo @Lar1sa this should be part of Common Core
## 183 @rweingarten This is some real common core math shit...🙄
## 184 @nathanthetweet @jaehhan No the school system is corrupted. Is Common core still being used?
## 185 @IM_Communityitw @EquilibriaCC There are five common core issues in technology today: scalability, security, interoperability, speed and privacy. How will EquilibriaCC tackle these problems
## 186 @nathanmhansen Common Core math says it's 90%, +/-30.
## 187 @ScottBaio Common Core math.
## 188 @thehill dissects. I don't have the debt then how does it lift me up to pay off someone else's debt. Isn't that adding a burden and holding me down? If not please clarify using basic math (not common core).
## 189 If you confuse the word “women” with the word “jobs” it becomes a demonstration of Common Core English https://t.co/WXtFf754Ez
## 190 @WeAreTeachers Ever want to show students how to organize their capital ABC's in order? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #numbers #googleslides #kindergarten\n\nhttps://t.co/ToQzsCGsMd
## 191 @RyanAFournier @twiggyoh Common core math
## 192 @Rokatoh And you want to talk about me being a "dumbfuck?" How long did it take for you to surmise, despite me having a maple leaf as my avatar,that I'm Canadian? I heard that common core is terrible but you've shown me just how bad your lefty education system is! You're such a jackwad.
## 193 @RandPaul @MLB i'm about to boycott the pga not because of the players the commerical and press...remember the masters that bubba won obama was in the wh and he had common core comerical running...yep everyone in obama's school were dumbed down to the lowest common denominator and that's math
## 194 @RaheemKassam that is cause they are putting Race crap into our schools and COMMON CORE Raheem.. like 4+4= is not 8 but different answer..
## 195 People can change from standardized to common core to whatever else all they want. The principles and overall structure of American education is trash and emphasizes extrinsic based learning. I was literally worrying about what number I was in hs (class rank). LIKE WHAT https://t.co/C2j9gS84nJ
## 196 @TucsonMelissa Or find a group to create a home school micropod, and you can even find materials that are NOT common core. It’s time parents start using their “buying” power, and stop paying for schools to remain closed.
## 197 @KWilliams009 I was in math before you sir😂😭 I wasn’t even in school for common core💀 I just add it like I would do on paper idk
## 198 @hankgreen If you look up anything common core related, you’ll be able to find studies on it. Moving from a state without common core to one that does drastically changes my learning and still does even in college today. It’s absolutely wild.
## 199 @JoannaMang HOLY MOLY, I just went and found an article with an exercise to self-check whether I do... and I DO!\n\nAnd I just read this: "most common core exercises—crunches, abdominal twists and, when done incorrectly, planks—can all worsen the condition"😱
## 200 @MattWelch Everything's being weaponized. I think Common Core Math was brought into mix because it prevented those students who excelled in math from doing well & leveled out playing field. It also created a wedge between kids/parents because parents couldn't help https://t.co/NGS92gMXwO
## 201 @Teufelshunde_95 Agreed. Please add “no common core” to that list too.
## 202 @JakeSundall Algebra using common core methods 🤣
## 203 @TheUnitedSpot22 @Solmemes1 That’s that common core math!
## 204 @GPeaceelf I have realized to achieve the healthiest / Best lifestyle do everything the opposite of what Bill Gates would tell you \n-Eat real Meat\n-Get Sun \n-Don't wear a Mask\n-Don't Vax \n-Dont stay at the top 6 floors of Mandalay Bay\n-Teach your kids real math not common core
## 205 @ilDonaldoTrumpo I was never great at math!! That's how I know Common Core is garbage! ☺️😊
## 206 Common core at its finest...\n\n . @ New Windsor, Illinois https://t.co/UKFvMimRci
## 207 @rweingarten 115%???? Is that some of that common core math? It is literally impossible, not figuratively, literally impossible for 115% of mothers with young children to leave their jobs in 2020
## 208 so i got this off of a common core crystal... https://t.co/Bo65otMn3b
## 209 @anthonycwalker Is this that common core nonsense? I just use a calculator. My head hurts lol.
## 210 @oorderr @davidhogg111 @alan_gable Only a person whom has ‘Zero’ knowledge of how the economy works would say that or a person brought up on ‘Common Core’ or a fucking Communist.
## 211 @My_Truth_Today Common core for reset?
## 212 #trivia #Politics #opentdb #triviaBot #twitterBot\nCategory: Politics\nDifficulty: hard\nWhat year did the effort to deploy the Common Core State Standards (CCSS) in the US begin?\n A. 2012\n B. 2006\n C. 2009\n D. 1997
## 213 @aelfred_D Common Core math?
## 214 @Mama_Lexxxx What type of common core math is this? Just do 50+30-5 lol
## 215 @newsmax That's hilarious! Is this common core math? Trump had 51 million viewers. Biden had 10 million.
## 216 Adding Decimals Worksheet Common Core https://t.co/qKE2tN38Qt
## 217 So close to elementary and all it’s Common Core glory I can taste it... \n#WritingCommunity #amwriting https://t.co/oWO83nTg4s
## 218 @GCRLPlayer hating NCLB is the center left version of hating common core, very cringe, very sad
## 219 @JoJoFromJerz @mattgaetz You obv have a common core "education" to spell the way you do as 8f its funny or clever. What i find funny is you never met a double standard you didmt embrace, like calling ppl on right names, yet pretending to be a woke white woman, worthy of anything but being passed around
## 220 @RealDTrumpTweet I thought he was to old for common core math, guess not 🤦🏼♀️
## 221 That’s why I took my daughter out of school.While everyone is screaming,Open the schools..they are still forcing masks and making them run and do laps at recess for https://t.co/QU9oLePXXd daughter couldn’t breath-7 hours a https://t.co/3ffzLNj5S8 indoctrination-Race/Common Core! https://t.co/aMRNBL4jTz
## 222 @Freakzilla069 @FB_FilmAnalysis The salary cap doesn’t exist, every team manipulates the cap with bonus money so much there’s no penalty, Dak is $75m this year and only counts as $9m against the cap. Common core math right there. That’s why guaranteed contracts don’t exist.
## 223 @prageru @Facebook ❗️sensible method to stop climate change in its tracks 🌳PLANT MORE TREES 🌲green plants eat CO2 that makes for better natural farming 🦕– don’t those idiots parroting this 🦜remember their 8th grade science classes – or did they learn Common Core only
## 224 @iamtony_97 When I was subbing the elementary school this past year I was literally always confused during math 😂 common core confused me! I was always telling the students this is not how I learned.
## 225 @TheGreatFeather 💯right on Elaine! these idiots have tried for years with”common core” to dumb down our kids! Now these political lazy dumb a@@ teachers are totally democratic numbskull, educated beyond their intelligents , on vacation, drawing paydays... shut them down !!
## 226 @YogaPantHijinks @THSCAcoaches I didn’t think that Texas was a common core state. So that’s my bad. That makes absolute sense
## 227 Common core math! https://t.co/y5RQVGDRpq
## 228 @SparkyPatriot I say it started in the 60s with all those damn hippies, then the 70s when China got involved in American gov. and then Obama teaching the Common Core bs and just the absolute stupid stuff that’s blasted on SM, & now have all these movements for rights they think they don’t have.
## 229 @AlexBerenson Hey he used common core math so that answer may be correct.
## 230 Read it on PsychToday - Fear of Flying's Common Core https://t.co/jFa2g6FsDx https://t.co/xIFZPJ7Bu6
## 231 @Joj_uwu Idk person seems pretty different from common core
## 232 @rottsbot @OldSchoolCoug @rweingarten That explains why states are dropping out of it like flies then right?\nhttps://t.co/OEXMKXqHT2
## 233 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 234 @thegfmband With that common core math i bet it is
## 235 Having trouble keeping up with Democratic Common Core Tax Math but does that mean if a Couple make $200k each they will qualify? Is this "bracket" creep? https://t.co/OeGbvHOgon
## 236 Fear of Flying's Common Core. Psychology Today https://t.co/7nMRyt4YuC RT travel #airlines #IARTG #book travel https://t.co/AqV3aCp8Ba
## 237 @GinnyWThomas @RyanAFournier And you're math skills must be common core ...
## 238 5 of 5 stars to Common Core Standards for Middle Scho... by Susan Ryan https://t.co/NX24UQhEZM
## 239 That pesky your and you’re. Maybe they need to add that to Common Core English? https://t.co/havhUu3mBz
## 240 @LangmanVince 99% Is that the new common core math your using
## 241 @PadrePat3 @thebypassline That’s that common core crap... his skin looks a lot tighter on his face in this video... 🧐🧐🧐 Just Sayin!💁♀️
## 242 @WINDOCTORRX They are using common core math
## 243 @WeAreTeachers Ever wanted to show your students how to identify grade-level sight words? Originally designed for kindergartners, this lesson can be adapted for any grade level.\n\n#commoncore #technology #letters #sightwords #googleslides #kindergarten\n\nhttps://t.co/k5gZv73tL7
## 244 @PolitiBunny Evidently Jack uses Common Core math 😂
## 245 @emzanotti Challenge her to solve a Common Core math problem
## 246 @vanallenmark @MarkMeadows Common core math..? Are we really doing this?
## 247 Arkansas Mother Obliterates Common Core in 4 Minutes! https://t.co/RKeOTq9gx1 via @YouTube \n\nOVOMIT'S 'COMMIE CORE' CONFOUNDING OUR CHILDREN /GRANDCHILDREN & TAKING AWAY THEIR SELF-ESTEEM TO DO BASIC, SIMPLE MATH !!
## 248 Shhhh! With common core Math it's still a good idea to true believers. https://t.co/OagNmxyAyN
## 249 @kennethp1 @kirstiealley Home Econ is a useful skill. Keep that launch common core.
## 250 “your money is worth less, so the 2.75 you paid 6 months ago is actually more expensive than the 3.17 you’re paying now!”\n\nIs this that common core math? https://t.co/K7DMXgfLZU
## 251 I have been in the classroom in some form or another since 2005. My math books were bestsellers on Amazon (which sounds more impressive than it is). I was a writer on the engageNY curriculum—the most widely used and awarded Common Core math curriculum in America.
## 252 @Madrina334 @thehill Trump knew ESSA passed in 2015 restated Feds are unable to interfere with local choices such as curriculum or standards like Common Core!\nReagan found schools failing in 1980s. NCLB & Common Core raised the bar but LOCALS R responsible to make it work!
## 253 Biden must be using common core math to support his narrative that America is thriving . https://t.co/zfBE23Ke56
## 254 Screw it, I’m game let’s just target Common Core as racist!!!!\n2+2=5: Oregon Department of Education Pushes Course Claiming Math is Racist Because It Requires a Correct Answer https://t.co/IKYcDDifnh via @gatewaypundit
## 255 @DBreath2021 @Fenian1978 @LadyLantzALot @PopeJudasV @MalloryGates14 @MikeMooreDO You want students to memorize & follow algorithms without understanding how to apply knowledge to solve real world problems?\nFact check how standards like Common Core work & how they affect classwork.
## 256 @MsBrouillet @DianaOrrock @D_Honch @NevadaPolicyRI @Icoacheducators @JillDickmanNV @Wheeler4Nevada @PKONeillNV Common core gets a bad rap from people outside the industry. How you gonna have national tests but not national standards?
## 257 @ButwhatdoIknow3 @TheBabylonBee That there is common core math
## 258 @ASewcialist @IrishLeftist I had common core all the way through 12th grade. My first two semesters of college were calc and pre-calc and I got “D” and “C” respectively.\n\nThat really drove home how poorly-prepared common core had me. That and the “Teach by the test, not by the student needs” mentality.
## 259 @fairforall_org @DukePesta @Hillsdale At some point we must look at our Babies & know things are just not ok. The Children are crying Folks. The judgements need to stop. Teen suicide has almost doubled since Common Core & since Covid has risen 66% \n✓ Mental health is blamed as Big Pharma benefits doping up Children.
## 260 A reminder that the entire system is geared to producing ignorant bots who will get the 'powers' that be do whatever they want. The NEA is a huge culprit in the dumbing down of America. Head Start and Common Core are failures.\n\nTrump was fighting against this and winning. https://t.co/Fi4ejPMuxf
## 261 @ResisterChic @GeorgePapa19 Stop doing meth. Do common core.
## 262 [Download] EPUB Common Core English Workbook: Grade 1 English -> https://t.co/Ql2FQdrWzA
## 263 Mother to Son -Langston Hughes\nIf We Must Die-Claude McKay\nEl Beso - Angelina Weld Grimké\nBlack Baby- Anita Scott Coleman\n#Black HistoryMonth\n#HarlemRenaissance\n#Poetry\nAnalyze sentence and stanza #rl6.5\n#CommonCore\n#CCSS https://t.co/xxd8Rvb6ZS
## 264 @Isabell87837456 I totally agree and one of those tools are common core
## 265 @JakeSwearingen facetune is common core now
## 266 Have #Common-Core Standards ruined our children's #handwriting? https://t.co/KGjSwVIJW9
## 267 nobody knows how to do common core math ? 😭
## 268 Obama s illegal executive order on immigration protect the Second Amendment end Common Core rebuild the country going into and out the presence of Salvia info.
## 269 @MrGmpls @bobsonwong I'll never forget sitting in a Congressman's office a few years ago and his words (paraphrasing here). "No one (at federal legislation level) is going to talk about Education for ten years, Common Core was just a huge, no-win politically, disaster."
## 270 fuck common core all my homies hate common core
## 271 @JRubinBlogger So @SenateDems minus Joe Manchin = 50 Senators. That takes some effin’ common core math to get to that number. I won’t even go into the second half of the #CrazyJen tweet.
## 272 @AOC Why don’t you help high schoolers who don’t understand common core.
## 273 @lauriepfohl @TrumpWarRoom @RandPaul Good luck with that Laurie. I'm pretty sure DJT accepted roughly four (that's $4.00 for all you common core grads) to work for all of us for four years(that's 4 years) making you lil' NPR nursery rhyme \nNULL AND UNHEARD
## 274 @NYGovCuomo Meanwhile everyday there's at least another homicide in NYC. Are those numbers added into the COVID deaths? I'm sure one way or the another one has to do with the other. I flunked teaching my kid common core math so I'm not the one to figure it out.
## 275 @MaggieforMO I think Common Core did public schools in from our perspective. We observe a lack of analysis, problem solving, and application skills in our teens’ classrooms. The public high schools promote the basic minimum graduation requirements.
## 276 @bigheadnolan79 I am against the war against Common Core that avoids discussing grad needs & how to get it!\nTop priority is a process to improve. The chaos of the CC issue is proof!
## 277 @steaknbacon13 @KeithMalinak Common Core...
## 278 @EEElverhoy seems like a common core issue going back to 2013, when a meaningless Algebra class was removed?\n\nNot finding the other two assertions of Steve's tweet?
## 279 @ajlamesa @alexanderrusso Most common core standards have a few grades where the skills are developed.\n\nOpen the schools back up and just teach where the kids are. They will eventually get to where they need to be.\n\nBut this needs to be done in person. Virtually learning does not work for the majority.
## 280 West Virginia Department of Education needs to so better. They are not doing well in pandemic. Stop using bad curriculum like common core..Stop blaming parent and kids. #WVr https://t.co/5jMarXHdOH
## 281 Common core math. https://t.co/GtCLRdFONe
## 282 @sofain I fucking HATE common core I’ve gotten into it with both my kids math teachers about this bullshit
## 283 @PanhandlAs Coach, it’s that common core math.
## 284 @PaulieD55 Unity through Division, the common core math that is the Democratic Party.
## 285 @iRunWithLula Black people & Democrats have ran Charm City for 50 years. What the excuse there? You guys in 30 years will still have isolated black poverty centers in these urban areas over populated bc they use them for votes & census. They never cared. Common core has reduced scores
## 286 @BrentTerhune Is this a common core math problem?
## 287 Zoom detention?!? Thats bullshit. I got two 4th graders on zoom school right now & honestly I could not do what they are doing every day..(not just common core math 😂☠️) https://t.co/RzeBN6aBvM
## 288 @nascarcasm The F-Word is taught in Common Core English starting in the 3rd grade in New York City
## 289 Why Common Core failed https://t.co/BkNRKc1Blf via @BrookingsInst
## 290 This! I moved to Salinas, CA with no job and a fiancée making slightly above poverty at a newspaper. Got fired selling used cars. Worked a boring-ass temp job checking grammar for Common Core tests. Then I aimed big for a tech job in SJ and ended up getting one. Never looked back https://t.co/AQMl2pUnuj
## 291 @karol Common Core Math idiots, they just don't get it.
## 292 Common core... and then this... hmmmm https://t.co/MSdBeHH2bp
## 293 @NikkiHaley @EliseStefanik Confucius is like American universities,Goethe Institute,Alliance Francaise around the world.China bashing is useless. We need to invest in green new deal,infrastructure,common core and relevant curriculum. Appointing of judges is not a policy for development. Let’s compete & win
## 294 @Matt_Shealy14 Lindsey Graham. If Common Core were a US Senator
## 295 NEW: 8-Day JHS Teachers Common Core Programme (CCP) workshop postponed by GES/NaCCA https://t.co/GxO2t44Cgw
## 296 2/Learning Cursive Isn't Required Anymore\n\nWith Technology, Is There A Need For Cursive Writing In School Now?\n\nCommon Core not requiring teaching cursive in elementary school is an abject failure in American education that'll make US less competitive. \nhttps://t.co/96swwIdA5M
## 297 @kylamb8 Common Core math
## 298 @RyanAFournier Common core math
## 299 OMG IT'S BACK!!!! Remember when Common Core was Satan? Doesn't seem like that long ago. Also remember when @ScottElliott_IN got lectured by Glenda Ritz for pointing out the "new" Indiana curriculum standards looked pretty much like Common Core. https://t.co/hHSqafjIx6
## 300 @hchanooo It's the way they started teaching math to the kids when we were in highschool (early 00's). Breaking it down into a thousand little steps to help kids better visualize the process.\nhttps://t.co/2YdsqxsV7g
## 301 Common core math is just demonic math
## 302 @mrobmused @kimrussell7 This was the meeting where I addressed the actual problem with the rules surrounding Common Core. We not only lost the narrative as to why standardization doesn't increase student achievement, we quit having the conversations 😞https://t.co/LWxuu02fH1 at least in Idaho.
## 303 @PegGrafwallner We use the Common Core. We call it NVACS because we include NGSS and social studies standards.
## 304 @fawfulfan This is one change in common core math, because the process matters, they want you to use certain steps to get to the answer (even though you can get the correct answers in other ways). And there are actually pedagogical reasons to care about the process
## 305 @HistoryfromMrH Again, education focused on what it means to be human in advertently makes people more employable than the “college and career readiness” focus of Common Core and College Board. Nobody is inspired by utilitarianism.
## 306 i like when people think i'm smart \nbut i'm dumb, so i won't add sums up anymore \nand i barely even care that i'm forgetting all the common core
## 307 @wookiegr @NewsHour Not sure the Common Core crowd will see what you did there! Nice one heh.
## 308 @AJ_Sinner2 @jlcx5_2 I mean that could have been it to depends if you used common core math or not I guess
## 309 @snarkgrapefruit Is it common core math or normal human being math?🤣🤣🤣🤣
## 310 @DJMessi23 Is that common core math?
## 311 @winningatmylife I’ll bet none of these exceptional people had to study “common core” math in school. It would have been hard to calculate the orbital insertion trajectory with sticks, boxes, & circles.
## 312 @Chart0grapher @Bri_guystock @thehill 2/2 \n\nBut I get it. Taxes will suck. But they already get 1/4 of what we gain and they aren't the ones taking the risks. And iirc we can only write off 3k in loses yearly. Which if you make say 20k, then lose 10 you still owe then 2k. If my common core is right anyway. Lol
## 313 @Kevin_McKernan I would have thought that after the wreckage wrought on the US education system from Common Core, we would have politely started shunning Gates from public life. His ties to Epstein, I believe, were after Epstein plead guilty. It does not speak well of Gates either way.
## 314 @NonovUrbizniz @adrielhampton @davidslavick ?? You have any idea what common core did to the education system..or why Bill Gates is the largest owner of agricultural land in america..do you have any fucking Idea why rich assholes have these 'foundations' instead of fucking paying more in taxes?.maybe simp less
## 315 @ana08224723 @CortesSteve @GOP Apparently you only know common core math 2+2=5. That's the only way you could ever win an election and get away with. You know like having more votes counted than registered voters in the state.
## 316 @awbroussard05 The new common core math.🤣🤣
## 317 @jpcarew4 @martysalo @nypost But Desantis blames Common Core for decades of U.S. ed failure that even Reagan found in 1980s. 1834 North American Arithmetic book said "much of Arithmetic is practiced but little learned."\nDesantis proves U.S. produced generations of memorizers instead of flexible thinkers!
## 318 @HorridHenryVIII a good example of this is parents (homeschoolers, 'common core' opponents etc) who don't know math or think math is an innate biological talent, being ardent partisans of 40-50 year old math pedagogy; and not making the connection between those two things
## 319 @peebsnjay @processfairy Oh my. I think I am now an expert in Common Core, based on that explanation.
## 320 @catturd2 @WhosFibbing Common Core Math !\n\nHere is an example:\nIf a monkey had 6 bananas,\nand ate 3 of them, what color\nis the elephant at the circus ?\n\nIt makes ZERO sense !
## 321 @ASnilloc @pookaanniee @tishray @tradcatmaria The husband and wife come before the children! Why would she want to ignore his needs & an opportunity to serve him whose labors provide for them all?\n\nAnd why isn't she homeschooling? It's easier than putting up with driving, lunches, & idiotic common core homework assignments
## 322 @EricRoit @NYGovCuomo I know elementary school math as taught to me in the '70's. I said common core math which is more complex and requires more steps to come up with the same answer.
## 323 Adults will clown on common core math as if they can still remember how to add fractions like idk man you’re walking proof that something needed changing lmao
## 324 The Ghana Education Service (GES) has announced that it has indefinitely suspended the implementation of the Common Core Program (CCP) for Junior High Schools (JHS) teachers following the action by participants. https://t.co/63kvYHaDta https://t.co/ksqwgZ6oNY
## 325 @DadJokeRob @virtualmcg @scrowder i have no idea what you’re going for here, but it’s spelled asteroid, common core teaches phonics and proper english, and you made yourself look like an idiot
## 326 Common Core...and/or Chicom ESL. 🤣 https://t.co/r08GasC0Ad
## 327 @EpochOpinion @EpochTimes @ZwaagstraM Common Core. Why did Obama want American students to be such dumbasses?
## 328 @0ctoberReignz @gavi As most already knew, human life has lost considerable value on the open market over the past 13 or so years.\nChalk it up to ongoing results of the "Philosophy of Economics" curriculum of common core "Humanomics".
## 329 @VoteRIGHTinAZ @redwildcat Common Core 2.0. Yes we have read them.
## 330 @IJasonAlexander Obviously we some sort of common core equation to figure this one.
## 331 My new DC project posted! My project will help raise funds for a second monitor. This will allow me to video chat and present at the same! Who’s going to be my first donor? \n\nhttps://t.co/9zroH7Fybu\n\n@DCAGivingPage\n@gatesfoundation\n@support_a_teach\n@GivingClassroom\n@BlueGiving
## 332 Common core math. https://t.co/IGLbd4etKT
## 333 @jefffcarr @RickUcchino Nick Krall’s answers are as clouded as common core math
## 334 ⚘ Read Online Peter Pan (Graphic Revolve: Common Core Editions) => https://t.co/mfdCFPO2xQ\n\nღ
## 335 “The absurdity of mandating testing in the middle of a pandemic will fuel the growth of the national test resistance movement…a robust opt-out movement helped break the back of …Common Core tests” #maedu #mapoli @Mass_CPS @AFTMass https://t.co/JGmizRtITk
## 336 #mypaulandchain and I talking about renting an airbnb:\n\nPaul: so this says we can only have 4 guests—\nMe: no, 10...\nPaul: —in addition to the 6 already there.\nMe: yeah, you’re doing this Common Core Math shit and I’m just getting to the answer
## 337 @Psc725 @JFNYC1 I just followed you and you’re down to 217 now. I had 1,087 yesterday and now it’s 1,075. Must be common core math LOL
## 338 @UtopiumTinkerer Common core, high achiever.
## 339 @rweingarten This must be common core mathematics because I do not know how you get more mothers to leave their job?
## 340 @MarionTsf Common core math...
## 341 @DogsBCool @RhondaT79833457 @Doku_HL_SD @Thumper18560818 @simon_r_katz @the_lorean @kashmirismine @EMMYreincarnate @1Quetzalcoatl1 @gigibuddapdx @TomDoubting @ListNever @OwnedByCats1973 @JDW714 @cujobassplayer @AllisonEMoss2 @Vickie627 @Black_is_back5 @Vincenz42493578 @JayEstus @LisaNicholo @boodacat2020 @giddy_bunny @KingCujo @skiroita @keith37283360 @WorkerBee41 @AlgrenQ @shrekthepunk @catlady111310 @jdd2169 @SBDSoultrain @mlodgeg @GOPkleptocrat @x31Rufus @BPR_HODL @AdrenochromeD @Brion_TV @Mongo3804 @N3ILA_ @haybailsdj @Covidity3 @Wilson90732929 @jonathanhgruber @LiquidD81 @th3Cloud9 @lloomer80 @KodiaksPal2 @instinctnaturel @realDonaldTrump 😂 small call. See I didn’t have to deal with the woke crap since common core was not allowed in my state. We were the first stage to say NO which was a great call so it wasn’t in our public schools and then I also went private and identity politics and CRT is not allowed there.
## 342 @BoogerLeader @IdkImean @MalloryGates14 @MikeMooreDO I tried to learn common core, and it's just harder. \nIt takes up so much more time when you're doing anything else. \nIf I have to do a 10 step process to find out what 400-220 is, I don't think it's even worth trying to teach a kid.
## 343 @baggerbag43 @catturd2 A common core education sure outs an idiot.
## 344 @NDpendentPDX Welcome to Common Core Policing. Funded by Soros of the Open Society Foundation and designed to essentially neuter law enforcement under the guise of woke ideology where everything offends. This is why the budgets were cut and CP came in with 'Free training'. The dangling carrot.
## 345 @KramerJosh Seems in a lot of areas of life, folks force change just for changes sake. Sometimes it works, sometimes it doesn’t. It’ll be interesting to see if common core math sticks around or not.
## 346 Is this common core math 3 penalties to 1 equals offset
## 347 I'll never figure out this common core math. https://t.co/meRQgjkYwD
## 348 @awstar11 New math? Common core math? Kindergarten math?
## 349 @RogerDanison @CoachBailey45 Analytics is the Common Core of baseball. Could you do it that way? Sure, but why would you want to?
## 350 @dapperestdog @midderska @bekkieann @CHAMPION316 @paniszczyn @Microsoft Property !== Speech.\n\nTwitter uses public infrastructure to operate, benefitted off tax subsidies, and violated Section 230.\n\nPlease upgrade your Common Core Education.
## 351 I cannot more vehemently express just how much Common Core has ruined education. 👎
## 352 @KeithGrant09 @JoeBiden The confederate flag and the entire existence of the Confederacy was started by southern, slave owning, and now to be erased by monument destruction, #Democrats. Time to study real history. Not #commoncore crap that’s indoctrinating millions.
## 353 @catturd2 Common core 😂🇺🇸
## 354 @BEisenhart My parents give me the theories, I run the Bayesian probability on those theories, then my parents say “huh, must be that common core math,”.
## 355 @masonpelt Fighting the Krauts, demanding answers about Common Core
## 356 “NO regulation to protect homeschooling students.”\n\nProtect homeschooled children from what? Awful common core/standardized testing which doesn’t teach creativity or help students?\n\nSure, some homeschool parents aren’t perfect but most public schools don’t do a great job as well. https://t.co/KKgCZf5tf5
## 357 RT@EdNCES: NOW AVAILABLE: 2019–20 Common Core of Data (CCD) Universe Files\n\nAccess NEW #CCD data at the state, district, & school levels, including staff counts by professional category, and student membership by grade, race/ethnicity, & sex: … https://t.co/ebXrk01QYz
## 358 Teachers: Teach Common Core the “Fun Way” with Study Guides https://t.co/47AzB8QxYy
## 359 @JackPosobiec Common core maths. Man - penis + 2 breasts = birthing person.
## 360 Boomers learned about common core math and never shut the fuck up since
## 361 Check out our online reading and writing summer tutoring programs where we teach students to read complex academic English. #commoncore #english https://t.co/b0kaBEHpI1 https://t.co/bSXxNhDXeo
## 362 The New Democrats world. Authoritarian government run by those who think they are the elites. Good luck to all those peasant kids who@go to public school taught by rabid leftist union teachers in Common Core BS. https://t.co/1KRSzQ7wZu
## 363 @Virtual1nstinct common core be like;
## 364 @LLinWood Gag order https://t.co/VTdnQ4G7JZ
## 365 @ACTforAmerica #PelosiLovesTrump no spaces = common core one word. 😁
## 366 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 367 @TheAncientFire @dougstafford @Cernovich Common core is dumbing down our children
## 368 A feature, not a bug of progressivism. Look at Common Core https://t.co/zDkePbel6W
## 369 There is almost no situation\n\nr = 3.14\ncan't get you in or out of.\n\nCommon core math is a bane on society.\n\n× = MC Square is for those rare occasions.
## 370 @bob__miles I’m scared of them, but only because one of my big selling points is being decent at math, and they all grew up with the far superior common core math curriculum
## 371 Right after he retired from being an Expert in Education and foisting Common Core on US Children. \nGates is a Deranged Man and he is Dangerous. https://t.co/GoEnVBwCXI
## 372 @business Dumbass all you have to do is talk common sense and leave common core at the door where it belongs.
## 373 @WhatNelsonSaid @FoxNews To be fair, they only had a pencil or inkwell when the 1st Amendment was written!\nIt's not about guns, it's about rights. Common Core educated?
## 374 @ABorealis77 Grads who learned Common Core would understand why 'Ma & Pa kettle' math don't work!\nU.S. produced generations of adults who couldn't because thinking hurts them!
## 375 @NYCMayor @NYCSchools Bro stop! NYC DOE is a joke! The Common Core is trash, built by the majority of people that have never been in a classroom. I sure hope @DrBiden is able to completely dismantle it, because we need to do better for teachers and students!
## 376 @OneFool17 @HaZey_hM @LegendaryEnergy Yep we’ve spent 2 yrs building a network of like minded homeschooling folks due to SB276&SB277 I knew they were coming for children with the vax - follow your gut. Let alone the common core BS for a 1st grader just learning math and struggling w/reading. She’s ahead now wahooooo
## 377 Thank God #Texas banned critical race theory from schools.\n\nNow do common core @GovAbbott cuz the teachers are still pushing that crap also.
## 378 @JuryReporter @glennbeck Then explain why so many young people want socialism? It’s the way they’re taught in schools. And with common core and now taking away true history, we’re bound to repeat it and not a good thing.
## 379 @mehdirhasan 3% on Fortune 500 Billion company is a lot of money. Perhaps Hasan is having another TDS flair up. It also seems like math is hard for him. Must be a graduate of common core education.
## 380 @DaniScotchIrish @3434Maple @cachobweeney @JeffreySHarper1 @Pulpolover @MarilynCapps @Gatoram15 @GiveNoFuxMichy @MickDAmario @Regan0002112 @katz_mum @lauradenton1955 @WalterLapp @suziferg0806 @NicoletteMpls @ninigirl84 @nocovfefehere @acroperr @DavidOtte9 @HockeyFanGirl12 @TxsleuthUSA @marthaj44 @JAS16AZ @gracesru @POlubunmi @TexHellCat @22AngieMarie22 @catalishus2 @Brindle_Girly @UnimpressedTX @txsguy09 @brendap1949 @COConstitution1 @smc429 @factcheckyou1 TY! Did your daughter’s have Common Core on it? She did poorly the first time (ACT), so she’s been doing an online program to teach it to her. She took the ACT again last week, and is taking the SAT tomorrow. Poor thing has spent her entire Spring Break studying. Blech!
## 381 @__mersace Yup. And this is why I will fight anyone who says Common Core sucks. It's actually teaching you MATH not just memorizing steps.
## 382 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 383 Sooo apparently KOS-MOS is the rarest blade in Xenoblade 2?\nI present to you... KOS-MOS out of a common core without boosters~! Lucky~~ https://t.co/rDMB7bRQR5
## 384 @catturd2 Democrat common core math!
## 385 @jarvis_best @AliceFromQueens Found the Common Core grad
## 386 @mwp764 How am I supposed to help my 11 yr old with his common core math when it hurts my head?
## 387 @Guidofa77866371 @vinod_d007 @realDonaldTrump It has happened in pockets all over for decades. They got greedy & apparently used common core math to apply votes needed and over shot it🤣
## 388 @MizzterMonkee @NBCNews Common core, No Child Left Behind, Race to the Top, all federal initiatives that change education by creating national guidelines. School districts don’t always fund the new books or equipment to make it happen. And teachers are rarely invited to the table.
## 389 @crowcawer Common Core has serious problems, just not the ones the extreme right thinks it has. We need standards created by teachers, not testing companies and billionaires.
## 390 Adults without school-age kids that bitch about common core math should not be spoken to, or even looked at directly.
## 391 @sofain I’ve sat with my kids for hours trying to help them figure that BS out. Then show them the way I was taught they grasped the concept in less than 10 min and had all right answers. They both got F’s and notes sent home because it wasn’t the “common core way”
## 392 @TimSabean Common core??
## 393 @ASimplePatriot @Gregory52594205 Unless it’s common core for leftists. Then the answer is “purple”.
## 394 Taxing billionaires 100% still won't cover trillions of debt unless you're using common core math
## 395 @ArtOfWarNews defund the police!!! But fly the flag at half staff now that one of them has been killed. Not the civilians that paid for that building... just the police officer that we didn't want... Makes common core sense.
## 396 Schools Test-Drive Common Core - https://t.co/7IWbQMdDMY
## 397 @LovesTheLight School system was to make you less a threat!\n\nI still math rage!\n\nI tutored Organic Chemistry with ease in college\nbut when I tried to tutor a 3rd grader Common Core math in 2012 I remember thinking what the fuck have they done to math\n\nThey killed it!
## 398 Kindle Free Common Core English Workbook: Grade 7 English -> https://t.co/dTdmFuUCME\n\nCommon Core English Workbook: Grade 7 English Ace Academic Publishing pdf download\nCommon Core English Workbook: Grade 7 English Ace Academic Publishi
## 399 @Cernovich That's those common core type thinking folks...the " there isn't a true answer" type. Those are the morons.
## 400 @OldMemeArchive Depending on how old the person who made this was, it’s either a critique of the inefficiency Common Core or some kid upset that they have to learn the fundamentals before taking shortcuts
## 401 CCP Democrats math\n\nYou received $600 last time\nWe give you $1,400 this time\n\nThat is $2,000\n\nYou voted us in GA because we Promised we would give you $2,000\n\nWe the CCP Democrats support common core, that is how math works
## 402 @STEFisDOPE_ @jkasoro2 @Pluthoenian @Fdog51 @Hibzster Wow. #nclb #commoncore never disappoints!
## 403 @vegalitarian269 Common Core IS mandated, federally
## 404 @gvlob @LakesFirearmsTr @SebGorka UK Pearson Press has a monopoly on what our children are taught.\n\nPearson Press Publishing monopoly are behind Common Core and government corruption.\nhttps://t.co/fis3091Nnd\n\nhttps://t.co/ZwOM9DzF7D\n\nEverybody Hates Pearson\nhttps://t.co/dFqI9lbFSu
## 405 @BobbyShowFlake @ChrisLoesch Do you have a fully functioning brain? \n\nAdded in extra common core classes to your curriculum? \n\nJust asking.
## 406 Don’t get me wrong- I like common core. I just didn’t understand it til I had to really go through it with my daughter. I just now figured out why it’s so hated. No one in our generation gets it (until they have to, like me)
## 407 @Long_Tailed_Tit @MalloryGates14 @MikeMooreDO Everyone I know who does math in their head uses common core methods. Some of us can't do it in our heads, common core makes no sense and we need it written down in columns. Why can't they teach both?
## 408 Conservation Education and Activism https://t.co/XZX8gshJ6V
## 409 @SouzaJr Common core lol
## 410 Common core meet woke core? Virginia moves to end all advanced math classes before 11th grade as part of ‘equity-focused plan’ – https://t.co/tyqDf385Rn https://t.co/nRnobYtCk6
## 411 The Common Core State Standards had a small but statistically significant positive effect on math scores in its early years, and there’s no evidence that the standards reduced achievement when implemented, finds a new study. @V_McDougald explains: https://t.co/14RaGHsEm9
## 412 @WhatsTrending Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/XZg9Ro8lID
## 413 @christina_bobb @GreenBelinda Common core educated?
## 414 #CommonCore #CommonSense Knowledge is power site:Is this #compact style of writing #common or unique as editors say https://t.co/sxW9RHgWzm
## 415 @BookMeanderings A lot of the anti-Common Core stuff is intentionally made more ridiculous than Common Core really is. \n\nCommon Core might be:\n27 + 28 is the same as 20+20+8+7.
## 416 Ladies and Gentlemen, I give you Common Core https://t.co/xhajp137g7
## 417 @bridgetphoto @LaurenGruel @CNUSD and the kids are on the same page with what they are learning. Critical race theory has nothing to do with common core. CC also allows students to learn multiple methods to understand a concept.
## 418 @aiopastafarian @McNeelie @cringepolitik @briebriejoy "When you said she's "playing that game to own the libs" it suggested that she was in fact "doing being cynical".\n\n(I guess that's common core writing for ya)
## 419 @chelseahandler How many police interactions per year with people of color? How many police shooting in a year? TOTAL??? Your math is off stop using common core.
## 420 @Educatorslab #CultureEd \nA3: In Math Ed I believe I'm a pioneer/fighter for ditching the text & common core & presenting a course based on manipulatives, problem-solving. cooperative learning & presenting math as a unified subject relating the various strands, searching for gathering ideas.
## 421 So many people’s inclination to carry the one is a big reason why math interest drops at algebra & why common core is crucial to fixing that. https://t.co/cMCoOeivI4
## 422 "More than a decade after the 2010 release of Common Core State Standards in English language arts and mathematics, no convincing evidence exists that the standards had a significant, positive impact on student achievement." https://t.co/p0arrli7nb via @BrookingsInst
## 423 @RyanAFournier It's common core
## 424 Access PDF:\n☞ https://t.co/Zs8RaeGO9U\nFind for 【Grade 4 Test Practice for Common Core (Barron's Core Focus)】 [Download] EBOOK\n▶ by Kelli Dolan
## 425 People, don't be sheeps....uh, ur...I mean heeps, woeps, theeps. Not sure of the final form, but common core will figure out what we should learn and what should be taught.
## 426 A GREAT ROLE MODEL & HERO HANK AARON\nMay he REST IN PEACE\nIf C-19 has 99.8 recovery rate\nNeed 2b tested 2determine if have\nBased on 30 plus cycles or more\nw/Incentive 4medical diagnose\nif HCQ works w/ZINK\nWhy push 95% effective vaccine\nCOMMON CORE THINKING\nhttps://t.co/DU46tW6xt3 https://t.co/GvOoFgYZCU
## 427 @shoe0nhead Looks like common core has moved to art class
## 428 i am so tired of seeing dumb motherfuckers clowning on common core math\n\nit's not inefficient, it's just putting the visuals for people that struggle with the math\n\nit's literally just showing people how to do math\n\nthere's nothing wrong with it
## 429 @staceykate1973 a common core victim
## 430 @onepantleg @makinba92399597 @LeftAccidental @DineshDSouza @tedcruz Where's Trump's healthcare plan\nWhere's the surprising thing Sheriff Joe found in Hawaii\nWhen is Common Core being repealed\n\nBro, they make promises when they run for office that they likely can't keep. They usually blame other people, and they're usually right to do so.
## 431 Also my experience when testifying against Common Core in Georgia. Listen to the GA hearings chaired by Sen. Ligon and see how whistle blowing poll workers have been treated by Secy of State. Why I Am Joining The Jan. 6 DC March For Trump https://t.co/BGdiUHRi2e
## 432 Read it on PsychToday - Fear of Flying's Common Core https://t.co/jFa2g6FsDx https://t.co/xIFZPJ7Bu6
## 433 According to Common Core math, What does an upside-down house with a pancake on top add up to??
## 434 @JillFilipovic Or maybe people on the right want to teach our children things like god, love of country, real history not reimagined history, real math not common core garbage, a good work ethic. You know just all the really evil shit that the left hates that makes a productive person
## 435 @cutecanukgirl @robdoel It has been made clear;kids raised under Common Core slack math (2+2=5 because I feel I can explain it), or the students get pushed through because of social constructs, will fail any entrance exams and will not be hired. \n\nFeelings and excuses does not put Space X on Mars.
## 436 @adamscrabble The common core reading I saw from 2 recent high school kids was alarming. It was not even books but excerpts. Barely any real reading. Writing also limited and certainly nothing creative. Just enough to pass the ‘standards’
## 437 Couldn't agree more @jamesbshort #CurriculumPL will make the difference. Read: "In the years ahead, we should double down on investments to help teachers leverage the high-quality CCSS-aligned curricula .. ” https://t.co/4gj16AGBsf For guidance see https://t.co/iezEeUsmeh
## 438 @DrKarlynB @ChrisSununu They could easily just rebrand it with different name. I think they did similar with Common Core.
## 439 @ExcludedMuddle @richard5832 Schooling is not homogenous. Sorry, completely wrong.\n\nSingapore math curriculum for example >>>> common core.
## 440 @SheriffAlert Is this what we get with Common Core math?
## 441 It's amazing, but the more followers I get, the fewer followers I have. Down over 6K now since January 1.\nMust be common core math.
## 442 @RandyScottESPN I still teach my kids the “old” way, and all of the teachers I’ve talked to over the years agree with me. They admit, they don’t even understand this common core crap
## 443 @ThaddeusRussell It’s not their fault. They’re hearing this in the context of public school social studies and common core math.
## 444 Decoding does get a brief mention when they celebrate ("Thank Goodness") that"The Common Core's emphasis on high-level comprehension skills calls for a reversal of NCLB's focus on decoding and low-level literacy skills.".
## 445 @TimRunsHisMouth Nope, I thought common core would be dealt with. Never a word on it.
## 446 @CrnchyMama @AlypiusMaximus @misselisabeths @DeAngelisCorey @rweingarten Teacher perspectives, like parents depend on how implemented. I regret seeing a determined war against Common Core instead of focussing on what kids need.\nWhy complain about classwork that is a textbook issue instead of looking at goals & motives?\nhttps://t.co/nlNTxW6L8u
## 447 @SaranyanManohar @mathsjem @MissBsResources @mathsbox1 @atulrana @mathladyhazel @DirectorMaths @Just_Maths @MEIMaths @DrFrostMaths Common core sheets are good resource too
## 448 Flipping a grilled cheese: Common Core edition https://t.co/lXoVKOlpuk
## 449 @LibertyVigilant My Dad was complaining about that 30 years ago, LONG BEFORE COMMON CORE!\nCommon Core is a fix, NOT THE PROBLEM!\nThat is why Reagan recommended states & locals take responsibility & create standards in the 1980s.
## 450 @cc77200307 @FoxNews @NYGovCuomo Reread it again. Perhaps you are from the common core generation
## 451 A thread that I'm interested in: how the actors in the Common Core story keep reappearing in NY politics. Now: @MARCMORIAL standing next to @NYGovCuomo. Then: https://t.co/VjLPhXisuT https://t.co/k7FOXX3MSu
## 452 @matthayen Proofs are so fucking stupid I fucking hate that shit so much. “Prove it’s a triangle.” LOOK AT THE FUCKING THING IT HAS 3 CORNERS IT HAS A 90 DEGREE ANGLE IN THE FUCKER. FUCK COMMON CORE. COMMON CORE CAN SLIDE ITS GREASY TONGUE ACROSS MY SWEATY TAINT.
## 453 zoology belongs in the common core. Literally a matter of life and death! https://t.co/Xmk8fp8QIe
## 454 @tweettruth2me At this point, who knows what the real numbers are, does it exist, sure... does it exist in the context that was hysterically communicated, I doubt it... math is hard, numbers are hard, maybe this is a rounding error due to common core
## 455 The more I learn about common core math, the more stupid it seems.
## 456 @jerry6667 @Tex_968 @vjeannek @ScottTBrower1 FYI the source was listed on the bottom left. \n\nArgue with the U.S. Energy Department —\n\nObviously you graduated Summa cum laude in common core right?
## 457 @mschlapp He must have studied Common Core math!
## 458 @SHINee giving me the OOOMPH to get through 3rd grade common core effing math with an ADHD & autistic kiddo. 🤦🏻♀️🙏😍❤️
## 459 @rmahon87 @TheRising1776 @kylenabecker Common core math junkies and failures. I know you 🐏🐑 are good lil obedient 🐑🐏. Now look at the lil red number and explain how it dropped? Wait I know 2+2= Purple Chicken 👍. I would explain it for you but wouldn't want that lil 🐏🐑🧠 of yours to 🤯
## 460 @TimRunsHisMouth @mjohn55 The most important thing that needs to happen is to trash common core curriculum and/or gender studies. She did neither. I bid her good riddance.
## 461 @changingform Especially if they were indoctrinated by 12 years of Obama's Common Core Socialist propaganda.
## 462 @realDonaldTrump @TrumpWarRoom\nTHEY DEFAME YOU!\nTRUMP IS A BRAND\n$1 TRIL LOSS\nDEFAMING LOSSES\nHOTELS/CASINOS/WINERY/BOOKS\nPERSONAL APPEARANCES/APPRENTICE\n@Scavino45 @DanScavino @RudyGiuliani \n\nOBAMA $65 MIL BOOK DEAL-GAVE COMMON CORE CONTRACT TO PUBLISHER https://t.co/rtp8JYqac0!
## 463 Read Free 6th Grade Common Core Math: Daily Practice Workbook - Part II: Free Response | 1000+ Practice Questions and Video Explanations | Argo Brothers >> https://t.co/nP98vsUTng
## 464 Let’s do some common core math....1400+1400+1400= 77.28. Hey @POTUS what’s up with your boys/girls at the @USTreasury and @IRSnews
## 465 @News_8 If "common core" social studies is anything like math ... I can only imagine the fake history kids are being indoctrinated with!
## 466 @shskaercher I mean, I definitely learned a for both and taught it until common core “forced” a function relationships unit into our curriculum.\n\nTo me, B seems like a dream for kids to catch on over that formula fear.
## 467 @marisarose1212 @LisaMarieBoothe 51% approval . Minus our all the ppl governor’s in blue cities killed in pure negligence.\nYour common core math doesn’t add up
## 468 Online Tutoring Program Cuemath Launches In The US: Cuemath is a one-on-one online personalized math and coding program that follows the US Common Core and is accredited by https://t.co/x9E0dG0dvU is launching in the US. https://t.co/rN3J0XuJVb https://t.co/vfWnF0CVz0
## 469 @historyKehoe @KimStrassel @POTUS If you or a business gets a tax break/tax cut, this is going cost you or the business more $$$?\nIf you don’t have to pay tax on your lunch meal, your meal bill is going to cost you more? \n\n???? Is this common core math or #SenileJoe math????
## 470 @Saorsa1776 Quote any CC requirements as proof!\nCommon Core has NO such thing. Read its requirements.\nIt wants critical thinking but NO specific topics.
## 471 @valwayne @AFTunion @DNC @GOPChairwoman @GOPLeader @GOP @LeaderMcConnell @TuckerCarlson @SenateGOP Parents the Teachers Unions are NOT your Child’s friend. Common Core will dumb down your child and indoctrinate them into being Tools Of The Left. \nWhy do Democrats and Common Core fight so hard to Prevent Charter Schools, & Home Schooling. Communist Activists pose as Professors
## 472 @Centrists2Cents @thehill I doubt he even has $5B liquid. Even assuming he has that much liquid that would only give every Amazon employee a raise of 8700 dollars roughly, for one year. Or 66k if given only to Amazon drivers. \n\nCommon core math has made everyone arithmetically illiterate.
## 473 Same asshole that took away Maryland #LEBOR is the same asshole that made common core math
## 474 @CariKelemen @Lo_VVriter Common Core Physics. The New Science. 🙄 E= whatever you want it to be.
## 475 @roosterteacher @RepHartzler Ever hear of "Common Core?" The "Federal role in public education" is pretty pervasive.
## 476 Questions regarding how we arrived at a country full of 'compromised intellects'?\n\nThrow in 'No Child Left Behind', 'Common Core' and pretty much any university, MSM, & here we are.\n\nIt's going to take some doing to reverse the above and get back to some sense of sanity/normality
## 477 Common Core Math is a cancer to the American mind, I wish this was unbelievable but our leaders don't give a fuck. \n\nhttps://t.co/IUaUn7Z6vI
## 478 Fear of Flying's Common Core. Psychology Today https://t.co/7nMRyt4YuC RT travel #airlines #IARTG #book travel https://t.co/AqV3aCp8Ba
## 479 [Download] EPUB 7th Grade Common Core Math: Daily Practice Workbook - Part I: Multiple Choice | 1000+ Practice Questions and Video Explanations | Argo Brothers => https://t.co/Au7JdU18ez
## 480 "The overall status dropout rate decreased from 8.3 percent in 2010 to 5.1 percent in 2019" but think how low it would have been if Common Core hadn't doubled it. https://t.co/BWS51E3gYc https://t.co/6hiuMgl3oP
## 481 @fit__feminist I can't process common core.
## 482 @bungalow312 @SelenaCarrion @paul_emerich Common core doesn't do that -- it's not a curriculum.
## 483 @cob1706 @laurenboebert See that's the problem with common core math \nIt dont add up and it dont make sense
## 484 i'll never forgive my elementary school for teaching me common core methods to do math problems
## 485 Common Core R.1 Guided Practice Slides Template *Can be used with any text!* https://t.co/hkJ6lBnjUs
## 486 @Williewill037 @DavidJHarrisJr Your mom causes death. No like for real though. Our death rate is on the same level as other countries. We just have more people. Guess they didn't teach ratios in common core math.
## 487 @coyne_sheryl All of the Common Core program. is horrible and is just one more factor that dumbs down kids. Intentionally.
## 488 @BrianMSTL Must be some of that common core math. I guess I've been doing it all wrong since it seems everyone born in 1980 is now 40 (and has been for at least 15 days).
## 489 @Walmart just curious, did you use Common Core math for this? https://t.co/TwcnoXjrY2
## 490 @SaraCarterDC AOC does not like to debate real ideas because she can never win hair-brained Leftist common-core math theories, even with her #FakeNews comrades.
## 491 @cmmteach Concepts: what is a function, linear functions, rate of change \n\nProcedures: equivalency, simplifying expressions, solving equations \n\nBut Virginia doesn't use common core so the actual answers for her might be different
## 492 @KYSenateGOP 👏just like many other states have done. Let parents decide which school to send their kids since schools are getting out of hand on social agenda, just passing kids, teaching common core, etc. KY ranks low on education & this might make public schools fix that @JCPSKY\n@JCPSSuper
## 493 @kingron_b That's that common core bullsh*t math! Thay would make my kid 21 and he's definitely only 13.
## 494 I wrote an article two weeks ago about what an asshole Bill Gates is and how he single-handedly ruined America's educational system with CRT programs and Common Core. He has also ruined global health programs. And buys off media outlets in secret.\n\nRead:\nhttps://t.co/VTWoghS7BH
## 495 @BarkTwain @jaymills @LouSchoolBeat @kugler_suzanne If we are going to have an equitable education across the commonwealth, the state does have to at least stake out a common core of material that all students should learn. Otherwise, highly inequitable education will continue to exist in Kentucky, as it does today.
## 496 Divide and Conquer: An Adult Course in Common Core Division. Step-by-step guide for understanding and applying Common Core division concepts. This course contains videos teaching new ways to divide that students are learning in school today.\nhttps://t.co/W3YWh462d7\n@JHUAP @physor https://t.co/6IPOl1kR2O
## 497 Yes every place in our little town is looking for good help that can pass a drug test. Our people w/a good work ethic working 3&4 jobs some part time obviously. How did there get to be so many bc there are so many that refuse to work-basic math don’t need common core math 4 this https://t.co/8Zv1yvhbHa
## 498 [whispers] "This is Common Core math!" https://t.co/pJchLzV9n7
## 499 https://t.co/g7kzxYPfH6 does this make sense in that common core math?
## 500 @michaelmalice I don't think this guy knows how percentages work. But, common core. ;-)
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## 107 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, 0}
## 108 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 109 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 110 {0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0}
## 111 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0}
## 112 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 113 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 114 {0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.3, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0}
## 115 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 116 {-2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 117 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, -2.4}
## 118 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0}
## 119 {0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 120 {0, 1.3, 0, 0, 0, 0}
## 121 {0, 0, 0, -1.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 122 {0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, -1.8, 0}
## 123 {1.3, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 124 {0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, -3.133, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, -1.493, 0, 0, 0, -1.702, 0, 0, 0, 0}
## 125 {0, 0, 0, 0, 0}
## 126 {0, 1.9, 0, 0, 0, 0, 0}
## 127 {0, 0, 0, 0, 0, -0.7, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 128 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 129 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 130 {0, 0, 0, 0, 0, 0, 0, 0}
## 131 {0, 0, 0, 0}
## 132 {0, 0, 0, 0}
## 133 {0, 0, 0, 0, 0}
## 134 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 135 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 136 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 137 {0, 0, 0, 0, 0, 0, -0.3, 0, 0, 0, -0.2, 0, 0, 0, 0, 0, -3.9, 0, 0, 0, -2.553, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 138 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 139 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 140 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 141 {0, 2.826, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, -1.332, 0, -0.962, 0}
## 142 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 143 {0, 0, 1.8, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0}
## 144 {0, 3.1, 0, 0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0}
## 145 {0, 0, 0, 0, 0, 0, 0, 0, -1.6}
## 146 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 2.033, 0, 0, 0, 0, 0}
## 147 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 148 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 149 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, -2.5}
## 150 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 151 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 152 {0, 0, 0, 0, 0, 0}
## 153 {1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0.4637, 0, 0}
## 154 {0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 155 {0, 1.7, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0}
## 156 {0, 0, 0, 0, -1.5, 0, 0, 0, 1.5, 0, 0, -1.5, 0, 0, 0, 0, 0, -1.2, 0, 0, 0}
## 157 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 158 {0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.77835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0}
## 159 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0}
## 160 {0, 0, 0, 1.9, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0}
## 161 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 162 {0, 0, 2.193, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 2.2, 0, 0}
## 163 {0, 0, 0, 0, 0, 0, 0, 0}
## 164 {0, 0, 0, 0, 0}
## 165 {1.9, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 2.4, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 1.554, 2.072, 0, 0, 0}
## 166 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 167 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0}
## 168 {0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.7, 0, 0, -2.7, 0, 0, -2.5, 0, -2.7, 0, 0, 0, 0, 0, 0, -3.693}
## 169 {0, 0, 0, 0, 0, 0, 0}
## 170 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, -1.4, 0, 0, -3.1, 0, 1.5}
## 171 {0, 0, 0, 0}
## 172 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 173 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7}
## 174 {0, 0, 0, 1.9, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 2.393, 0, 2.7637}
## 175 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6}
## 176 {0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 1.7, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0}
## 177 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 1.9, 0, 0}
## 178 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 179 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.083}
## 180 {0, 0, 0, 0, 0, 0, 0, 0}
## 181 {0, 0, 0, 0, 0, 0, 0, 0, -3.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, -1.6, 0, 0, 0, 0, 0, 0}
## 182 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 183 {0, 0, 0, 0, 0, 0, 0, 0, -2.6}
## 184 {0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 185 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7}
## 186 {0, 0, 0, 0, 0, 0, 0, 0}
## 187 {0, 0, 0, 0}
## 188 {0, 0, 0, 0, 0, 0, 1.11, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, -1.5, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, -0.962, 0, 0, 0, 0, 0, 0, 0}
## 189 {0, 0, -0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0}
## 190 {0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 191 {0, 0, 0, 0, 0}
## 192 {0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.05, 0, 0, 0, 0, 0, 0, -3.75, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 193 {0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, -1.6, 0, 0, 0, 0, 0}
## 194 {0, 0, 0, 0, 0, 0, 0, 0, -0.8, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0}
## 195 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 2.233, 0, 0}
## 196 {0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0}
## 197 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, -0.4}
## 198 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 199 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0}
## 200 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, 2.2, 0, 0, 0, 0, 1.1, 0, 0, 0, 0.8, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1.258, 0}
## 201 {0, 1.1, 1.3, 0, -1.2, 0, 0, 0, 0, 0, 0}
## 202 {0, 0, 0, 0, 0, 0, 0}
## 203 {0, 0, 0, 0, 0, 0, 0}
## 204 {0, 0, 0, 0, 0, 0, 0, 0, 0, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 205 {0, 0, 0, 0, -2.294, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 206 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 207 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0}
## 208 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 209 {0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, -2.1, 2.35}
## 210 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 211 {0, 0, 0, 0, 0}
## 212 {0, 0, 0, 0, 0, 0, 0, -1.4, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 213 {0, 0, 0, 0}
## 214 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.35}
## 215 {0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 216 {0, 0, 0, 0, 0}
## 217 {0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 0, 0, 0, 0, 0, 0}
## 218 {0, -2.3, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, -2.6567}
## 219 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.95, 0, 1, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0.75, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0}
## 220 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 221 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 222 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.5, 0, 0, 0, 0, 0.2637, 1.48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 223 {0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 224 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 225 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, -1.5, -2.3, 0, 0, 0, 0, 0, -2.57835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 226 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7637, 0, 0, 0, 0}
## 227 {0, 0, 0, 0}
## 228 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 229 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 230 {0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0}
## 231 {0, -0.4, 0, 0, 2.2, 0, 0, 0, 0}
## 232 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0}
## 233 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 234 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 235 {0, -0.85, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 236 {-2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 237 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 238 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 239 {0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 240 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 241 {0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 242 {0, 0, 0, 0, 0, 0, 0}
## 243 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 244 {0, 0, 0, 0, 0, 0, 0, 0}
## 245 {0, 0.3, 0, 0, 0.8, 0, 0, 0, 0, -1.7}
## 246 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 247 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 248 {0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 1.8, 0, 0}
## 249 {0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0}
## 250 {0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 251 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 2.593, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0}
## 252 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, -1.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.95, 0, 0, 0, 0}
## 253 {0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0}
## 254 {-0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 255 {0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, -1.7, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0}
## 256 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 257 {0, 0, 0, 0, 0, 0, 0, 0}
## 258 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 259 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.036, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, -1.2, 0, -3.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 1.6, 0, 0, 0}
## 260 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 1.3, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, -1.5, 0, 0, 0, 2.4, 0}
## 261 {0, 0, -1.2, 0, 0, 0, 0, 0}
## 262 {0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 263 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0}
## 264 {0, 0, 0, 1.793, 0, 0, 0, 0, 0, 0, 0, 0}
## 265 {0, 0, 0, 0, 0, 0}
## 266 {0, 0, 0, -2.1, 0, 0, 0, 0}
## 267 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 268 {0, 0, -2.6, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 269 {0, 0, 0, 0, 0.666, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, -3.1}
## 270 {-2.5, 0, 0, 0, 0, 0, -2.7, 0, 0}
## 271 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 272 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0}
## 273 {0, 0, 0, 1.9, 2, 0, 0, 0, 0, 2.2, 1.3, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 274 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 275 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, -1.7, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0}
## 276 {0, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, -0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 1.9, 0, -2.7, 0, 0, 0, 0, 0, 0}
## 277 {0, 0, 0, 0}
## 278 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 279 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 280 {0, 0, 0, 0, 0, 0, 0, 0, 2.193, 0, 0, 0, 0, -0.814, 0, 0, -1.2, 0, -2.5, 0, 1.5, 0, 0, -2.2, 0, 0, 0, 0, 0}
## 281 {0, 0, 0, 0}
## 282 {0, 0, 0, -3.726, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.8}
## 283 {0, 0, 0, 0, 0, 0, 0}
## 284 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7}
## 285 {0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.332, 0, 0, 0, 0, 0}
## 286 {0, 0, 0, 0, 0, 0, 0, -1.7}
## 287 {0, -1.5, 0, -2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 288 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 289 {0, 0, 0, -2.3, 0, 0, 0}
## 290 {0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, -2.02165, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 291 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 292 {0, 0, 0, 0, 0, 0, 0}
## 293 {0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8}
## 294 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 295 {0, 0, 0, 0, 0, 0, 0, 0, 0, -0.8, 0, 0}
## 296 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0.407, 0}
## 297 {0, 0, 0, 0}
## 298 {0, 0, 0, 0}
## 299 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 1.5, 0, 0, 0}
## 300 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.4363, 0, 1.9, 0, 0, 0, 0}
## 301 {0, 0, 0, 0, 0, 0, 0}
## 302 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.962, 0, 0, 0, 0, 0, 0, 0, -0.962, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 303 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 304 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0.3, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0}
## 305 {0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0}
## 306 {0, 0.75, 0, 0, 0, 0, 0.85, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.882475, 0, 0, 0, 0, 0, 0, 0}
## 307 {0, 0, 0, -0.962, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, -0.6}
## 308 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 309 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 310 {0, 0, 0, 0, 0, 0}
## 311 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 312 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.85, 0, 0, 0, 0, 0, 0, 0, 0, 3.6, 0, 0, 0, 0, 0, 0, 0, -1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.95, 0, 0, 0, 0, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.525}
## 313 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, -0.814, 0, 0, 0, 0}
## 314 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8637, -2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 315 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.8, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 316 {0, 0, 0, 0, 0, 0}
## 317 {0, 0, 0, 0, 0, -2.55, 0, 0, 0, 0, 0, 0, 0, -3.45, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.35, 0}
## 318 {0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 319 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 320 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 321 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, -1.5, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0}
## 322 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 323 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 2.45}
## 324 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 325 {0, 0, 0, 0, 0, -0.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.25, 0, -3.45}
## 326 {0, 0, 0, 0, 0, 0}
## 327 {0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0}
## 328 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 1.693, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 329 {0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0}
## 330 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 331 {0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 332 {0, 0, 0, 0}
## 333 {0, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0}
## 334 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0}
## 335 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 336 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, -2.6, 0, 0, 0, 0, 0, 0, 0}
## 337 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.083}
## 338 {0, 0, 0, 0, 0}
## 339 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4637, 0, 0}
## 340 {0, 0, 0, 0}
## 341 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.933, 0, 0, 0, 3.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 342 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.9, 0, 0, 0, 0, 0}
## 343 {0, 0, 0, 0, 0, 0, 1.3, 0, 0, -2.3}
## 344 {0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0}
## 345 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 346 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 347 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 348 {0, 0, 0, 0, 0, 0, 0, 0}
## 349 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.65, 0, 0, 0, 0, 0.45, 0}
## 350 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, -2.4, 0, 0, 1.3, 0, 0, 0, 0, 0}
## 351 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0}
## 352 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.7, 0, 0, 0, 0, 0, 0, 0, 0, 1.184, 0, -0.7, 0}
## 353 {0, 0, 0, 0}
## 354 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 355 {0, -1.5, 0, 0, -0.9, 0, 0, 0, 0}
## 356 {-0.9665, 0, 0, 0.8, 0, 0, 0.8, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0.8, 0, 0.85, 0, 0.65, 0, 0, 0, 0, 1.35, 0, 0, 0, 0, 0, 0, 0, 4.65, 0, 0, 1.65, 0}
## 357 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 358 {0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0}
## 359 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 360 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.85, 0, 0}
## 361 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 362 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 363 {0, 0, 0, 0, 1.5}
## 364 {0, -1.4, 0, 0}
## 365 {0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0}
## 366 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 367 {0, 0, 0, 0, 0, 0, -0.5, 0, 0, 0}
## 368 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 369 {0, 0, 0, -0.907, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 370 {0, 0, -0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3.75, 0, 0, 0, 0}
## 371 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0}
## 372 {0, -2.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.2, 0, 0, 0, 0, 0, 0, 0, 0}
## 373 {0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 374 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0}
## 375 {0, 0, 0, -1.2, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0}
## 376 {0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0}
## 377 {1.5, 1.1, 0, -2, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0}
## 378 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.406, 0}
## 379 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 380 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 381 {0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 382 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 383 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.332, 0}
## 384 {0, 0, 0, 0, 0}
## 385 {0, 0, 0, 0, 0, 0, 0}
## 386 {0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0}
## 387 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 388 {0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 389 {0, 0, 0, 0, -0.3, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0}
## 390 {0, 0, 0, 0, 0, -2.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 391 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 392 {0, 0, 0}
## 393 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 394 {0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0}
## 395 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -5.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.333, 0, 0, 0, 0}
## 396 {0, 0, 0, 0, 0, 0}
## 397 {0, 0, 0, 0, 0, 0, 0, 0, 0, -1.060825, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0.75, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.75, 0, 0, 0, 0, 0, 0, -5.25, 0}
## 398 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 399 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.332, 0, 0, 0, 0, 0, -1.3}
## 400 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 401 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0}
## 402 {0, 0, 0, 0, 0, 2.8, 0, 0, 0, 1.184}
## 403 {0, 0, 0, 0, 0, 0}
## 404 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.9, 0, 0}
## 405 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 406 {0, 0, 0, -2.1, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.493, -1.47835, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0}
## 407 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 408 {0, 0, 0, 0, 0}
## 409 {0, 0, 0, 2.35}
## 410 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 411 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.2, 3.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 412 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 413 {0, 0, 0, 0, 0}
## 414 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 415 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.793, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 416 {0, 0, 0, 0, 0, 0, 0, 0, 0}
## 417 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 418 {0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, 0, 0, 0, 0, 0, 0, 0}
## 419 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.2, 0, 0, 0}
## 420 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 421 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 422 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.258, 0, 0, 0, 0, 0, 0, 0, 0.8, 2.6, 0, 0, 0, 0, 0, 0, 0}
## 423 {0, 0, 0, 0}
## 424 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 425 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.481, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 426 {0, 3.833, 0, 0, 0, 3.333, 0, 0, 0, 0, 0, 0, 3.233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0}
## 427 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0}
## 428 {0, 0, 0, -2.193, 0, 0, -2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.554, 0, 0}
## 429 {0, 0, 0, 0, -1.1}
## 430 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 431 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 432 {0, 0, 0, 0, 0, -2.2, 0, 0, 0, 0, 0, 0}
## 433 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.8, 0, 0, 0}
## 434 {0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0, 0, 0, 1.5, 1.1, 3.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, -3.693, -2.87835, 0, 0, 0, -1.9, 0, 0, 0, 0, 0}
## 435 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 436 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.25, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.35, 0, 2.1, 0, -2.109, 0, 0, 0, 0, 0, 0}
## 437 {0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 438 {0, 0, 0, 0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 439 {0, 0, 0, 0, 0, 0, 0.222, 0, -2.393, 0, 0, 0, 0, 0, 0, 0, 0}
## 440 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 441 {0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 442 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 0, -1.6}
## 443 {0, 0, 0, 0, 1.258, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 444 {0, 0, 0, 0, 0, 0, 0, 0, 2.7, 1.5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 445 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 446 {0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, -1.8, 0, 0, 1.4, -2.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 447 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0}
## 448 {0, 0, 0, 0, 0, 0, 0, 0}
## 449 {0, 0, 0, 0, -0.8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.80042, 0, 0, 0, 0, 0.8, 0, 0, 0, 0, 0, 0, 1.1, 0, 0, 0, 0}
## 450 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 451 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 452 {0, 0, 0, 0, 0, -2.97135, 0, 0, -2.993, 0, -2.8637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -4.033, -3.233, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 453 {0, 0, 0, 0, 0, 0, 0, 0, 0.1, 0, 0, 0, -2.9, 0}
## 454 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.5, 0, 0, 0, -0.4, 0, 0, -0.4, 0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0}
## 455 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.693, 0, 0}
## 456 {0, 0, 0, 0, 1.533, 0, 0, 0, 0, 0, 0, 0, 0, -1.4, 0, 0, 0, 1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 457 {0, 0, 0, 0, 0, 0, 0, 0}
## 458 {0, 1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 459 {0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0.95, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.15, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.333, 0, 0, 0, 0, 0, 0, 0}
## 460 {0, 0, 0, 0, 1.093, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0}
## 461 {0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, -1}
## 462 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.033, 0, -2.433, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 463 {0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 464 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 465 {0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, -0.4, 0}
## 466 {0, 0, 0, 0, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -2.2}
## 467 {0, 0, 0, 2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -3.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 468 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 469 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 470 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.9665, 0, 0, 0, 0, 0, 0, 0, -0.65, 0, 0, -2.8995, 0, 0}
## 471 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.628, 0, 0, 0, -2.3, 0, 0, 0, 0, -1.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.6, 0, -0.693, 0, 0.3637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 472 {0, 0, 0, -1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 473 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 474 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3, 0, 0, 0}
## 475 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0}
## 476 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8, 0, 0, -1.2, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 477 {0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0.85, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, 2.775, 0}
## 478 {-2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 479 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.1, 0}
## 480 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.65, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 481 {0, 0, 0, 0, 0, 0}
## 482 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 483 {0, 0, 0, 0, 0, -1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 484 {0, 0, -0.814, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.7}
## 485 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 486 {0, 0, 0, 0, 0, -2.9, 0, -1.11, 0, 0, 0, 0, -2.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 487 {0, 0, 0, 0, 0, 0, 0, 0, -2.5, 0, 0, 0, 0, 0, 0, 0, -1.7637, 0, 0, 0}
## 488 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 489 {0, 0, 1.3, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 490 {0, 0, 0, 0, -1.11, 0, 0, 0, 0, 0, 0, 0, 0, -2.072, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 491 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 492 {0, 0, 1.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 493 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.7, 0, 0}
## 494 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2.1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 495 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 496 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 497 {1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 1.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1.4637, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 498 {0, 0, 0, 0, 0, 0, 0}
## 499 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0}
## 500 {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5}
## compound pos neu neg but_count
## 1 0.316 0.173 0.713 0.114 0
## 2 0.000 0.000 1.000 0.000 0
## 3 -0.818 0.000 0.564 0.436 0
## 4 -0.318 0.145 0.681 0.174 0
## 5 -0.956 0.000 0.708 0.292 1
## 6 0.000 0.000 1.000 0.000 0
## 7 0.660 0.375 0.625 0.000 0
## 8 -0.103 0.121 0.737 0.142 0
## 9 0.340 0.286 0.714 0.000 0
## 10 -0.026 0.097 0.803 0.100 0
## 11 0.000 0.000 1.000 0.000 0
## 12 0.273 0.062 0.938 0.000 0
## 13 -0.103 0.000 0.940 0.060 0
## 14 0.000 0.000 1.000 0.000 0
## 15 0.878 0.218 0.743 0.039 0
## 16 -0.542 0.000 0.870 0.130 0
## 17 0.755 0.204 0.736 0.060 0
## 18 -0.494 0.000 0.840 0.160 0
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## 480 -0.392 0.000 0.921 0.079 1
## 481 0.000 0.000 1.000 0.000 0
## 482 0.000 0.000 1.000 0.000 0
## 483 -0.402 0.000 0.870 0.130 0
## 484 -0.544 0.000 0.756 0.244 0
## 485 0.000 0.000 1.000 0.000 0
## 486 -0.872 0.000 0.769 0.231 0
## 487 -0.740 0.000 0.742 0.258 0
## 488 -0.477 0.000 0.916 0.084 0
## 489 0.318 0.173 0.827 0.000 0
## 490 -0.635 0.000 0.809 0.191 0
## 491 0.178 0.052 0.948 0.000 1
## 492 0.557 0.106 0.855 0.039 0
## 493 0.457 0.150 0.850 0.000 0
## 494 -0.735 0.000 0.871 0.129 0
## 495 0.000 0.000 1.000 0.000 0
## 496 0.000 0.000 1.000 0.000 0
## 497 0.829 0.171 0.792 0.038 0
## 498 0.000 0.000 1.000 0.000 0
## 499 0.000 0.000 1.000 0.000 0
## 500 0.361 0.161 0.839 0.000 1Take a look at vader_summary data frame using the View() function in the console and sort by most positive and negative tweets.
Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?
- YOUR RESPONSE HERE
Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their GitHub repository and I’ve copied and explanation of the scores below:
- The
compoundscore is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a ‘normalized, weighted composite score’ is accurate.
NOTE: The compound score is the one most commonly used for sentiment analysis by most researchers, including the authors.
Let’s take a look at the average compound score for our CCSS sample of tweets:
mean(vader_ccss$compound)## [1] -0.019974Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?
What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?
The author’s note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:
positive sentiment:
compoundscore >= 0.05neutral sentiment: (
compoundscore > -0.05) and (compoundscore < 0.05)negative sentiment:
compoundscore <= -0.05
Let’s give that a try and see how things shake out:
vader_ccss_summary <- vader_ccss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05, "negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ccss_summary## positive negative neutral ratio
## 1 157 174 169 1.10828Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side
In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below.
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
sample_n(500)## Rows: 8125 Columns: 8## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): text, source
## dbl (4): author_id, id, conversation_id, in_reply_to_user_id
## lgl (1): possibly_sensitive
## dttm (1): created_at##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.vader_ngss <- vader_df(ngss_sample$text)
vader_ngss_summary <- vader_ngss %>%
mutate(sentiment = ifelse(compound >= 0.05, "positive",
ifelse(compound <= -0.05,
"negative", "neutral"))) %>%
count(sentiment, sort = TRUE) %>%
spread(sentiment, n) %>%
relocate(positive) %>%
mutate(ratio = negative/positive)
vader_ngss_summary## positive negative neutral ratio
## 1 334 35 131 0.1047904How do our results compare to the CSSS sample of tweets?
5. COMMUNICATE
In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; and using simple word counts and word clouds to compare common words used in tweets about the NGSS and CCSS curriculum standards. Below, add a few notes in response to the following prompts:
One thing I took away from this learning lab:
One thing I want to learn more about:
Congratulations - you’ve completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select “Knit top HTML.” This will create a report in your Files pane that serves as a record of your code and its output you can open or share.
If you wanted, you could save the processed data set to your data folder. The write_csv() function is useful for this. The following code is set to not run, as we wanted to ensure that everyone had the data set needed to begin the second learning lab, but if you’re confident in your prepared data, you can save it with the following:
write_csv()Extra Credit (Optional)
Using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER.
If you’d like to use the data we’ve been working with for extra credit, let’s take a quick look at text analysis using bigrams, or tokens consisting of two words.
Bigrams
So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.
We can also use the unnest_tokens() function to tokenize our tweets into consecutive sequences of words, called n-grams. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.
To specify our tokens as bigrams, We do add token = "ngrams" to the unnest_tokens() function and setting n to the number of words in each n-gram. Let’s set n to 2, so we can examine pairs of two consecutive words, often called “bigrams”:
ngss_bigrams <- ngss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2)Before we move any further let’s take a quick look at the most common bigrams in our NGSS tweets:
ngss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 111,411 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 of the 630
## 4 in the 531
## 5 ngss https 455
## 6 the ngss 403
## 7 to the 318
## 8 for the 295
## 9 to be 272
## 10 on the 239
## # … with 111,401 more rowsAs we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we’ll need to use the separate() function from the tidyr package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, “word1” and “word2,” at which point we can remove cases where either is a stop-word.
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
separate(bigram, c("word1", "word2"), sep = " ")
bigrams_filtered <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word)
tidy_bigrams <- bigrams_filtered %>%
unite(bigram, word1, word2, sep = " ")Let’s take a look at our bigram counts now:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 45,507 × 2
## bigram n
## <chr> <int>
## 1 https t.co 6240
## 2 ngsschat https 721
## 3 ngss https 455
## 4 ngss ngsschat 236
## 5 ngss aligned 192
## 6 ngss standards 168
## 7 ngss science 154
## 8 science education 148
## 9 science standards 112
## 10 teachers https 106
## # … with 45,497 more rowsBetter, but there are still many tokens not especially useful for analysis.
Let’s make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:
my_words <- c("https", "t.co")Now let’s separate, filter, and unite again:
tidy_bigrams <- bigrams_separated %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")Note that since my_words is just a vector of words and not a data frame like stop_words, we do not need to select the word column using the $ operator.
Let’s take another quick count of our bigrams:
tidy_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 37,539 × 2
## bigram n
## <chr> <int>
## 1 ngss ngsschat 236
## 2 ngss aligned 192
## 3 ngss standards 168
## 4 ngss science 154
## 5 science education 148
## 6 science standards 112
## 7 ngss_tweeps ngsschat 96
## 8 science ngss 94
## 9 bmsscienceteach ngss_tweeps 92
## 10 approved approach 89
## # … with 37,529 more rowsYour Turn ⤵
Use the code chunk below to tidy and count our bigrams for the CCSS tweets:
ccss_bigrams <- ccss_tweets %>%
unnest_tokens(bigram,
text,
token = "ngrams",
n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
filter(!word1 %in% stop_words$word) %>%
filter(!word2 %in% stop_words$word) %>%
filter(!word1 %in% my_words) %>%
filter(!word2 %in% my_words) %>%
unite(bigram, word1, word2, sep = " ")
ccss_bigrams %>%
count(bigram, sort = TRUE)## # A tibble: 85,101 × 2
## bigram n
## <chr> <int>
## 1 common core 26735
## 2 core math 8249
## 3 core standards 683
## 4 core education 420
## 5 core curriculum 372
## 6 gt gt 262
## 7 bill gates 252
## 8 grade common 252
## 9 public schools 246
## 10 grade level 233
## # … with 85,091 more rowsWhat additional insight, if any, did looking at bigrams bring to out analysis?
- YOUR RESPONSE HERE
References
Note: Citations embedded in R Markdown will only show upon knitting.
Krumm, A., Means, B., & Bienkowski, M. (2018). Learning analytics goes to school. Routledge. https://doi.org/10.4324/9781315650722
Rosenberg, J. M., Borchers, C., Dyer, E. B., Anderson, D., & Fischer, C. (2021). Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter. AERA Open, 7, 233285842110242. https://doi.org/10.1177/23328584211024261
Silge, J., & Robinson, D. (2017). Text mining with r: A tidy approach. " O’Reilly Media, Inc.". https://www.tidytextmining.com
Wang, Y., & Fikis, D. J. (2017). Common Core State Standards on Twitter: Public Sentiment and Opinion Leaders. Educational Policy, 33(4), 650–683. https://doi.org/10.1177/0895904817723739
---
title: 'Public Sentiment and the State Standards'
subtitle: 'Unit 3 Case Study'
author: "Dr. Shaun Kellogg"
date: "`r format(Sys.Date(),'%B %e, %Y')`"
output:
  html_document:
    toc: true
    toc_depth: 5
    toc_float: yes
    code_folding: show
    code_download: TRUE
bibliography: lit/references.bib
csl: lit/apa.csl
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## 1. PREPARE

Data sources such as digital learning environments and administrative data systems, as well as data produced by social media websites and the mass digitization of academic and practitioner publications, hold enormous potential to address a range of pressing problems in education, but collecting and analyzing text-based data also presents unique challenges. This week, our case study is guided by my colleague Josh Rosenberg's recent article, **Advancing new methods for understanding public sentiment about educational reforms: The case of Twitter and the Next Generation Science Standards.**

We will focus on conducting a very simplistic "replication study" by comparing the sentiment of tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand public reaction to these two curriculum reform efforts. I highly recommend you watch the quick 3-minute overview of this work at [\<https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x\>](https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x){.uri}. Specifically, our Unit 3 case study will cover the following topics:

1.  **Prepare**: We'll take a quick look at Dr. Rosenberg's study, load packages we'll need for analysis, and learn how to use Twitter's API to retrieve data for those that created a Twitter Developer account.
2.  **Wrangle**: We focus on basic text mining processes such as text tokenization and stop word removal. Specifically, we will learn how to "tidy text" so we can perform some basic analyses such as retrieving word counts and term frequencies.
3.  **Explore**: In order to see what insight our data provides into answering our research questions, we will calculate some basic simple summary statistics from our tidied text and use data visualization to highlight some of these insights.
4.  **Model:** We learn about sentiment lexicons in our third lab and introduce the {vader} package to model the sentiment of tweets about the NGSS and CCSS state standards in order to better understand public reaction to these two curriculum reform efforts.\
    **Communicate:**

### 1a. Review the Literature

The **Unit 3 Case Study: Public Sentiment and the State Standards** is guided by a recent publication by [@rosenberg2021] *Understanding Public Sentiment About Educational Reforms: The Next Generation Science Standards on Twitter*. This study in turn builds on upon previous work by @wang2017 examining public opinion on the Common Core State Standards (CCSS) on Twitter. For Module 1, we will focus on analyzing tweets about the [Next Generation Science Standards](https://www.nextgenscience.org) (NGSS) and [Common Core State Standards](http://www.corestandards.org) (CCSS) in order to better understand key words and phrases that emerge, as well as public sentiment towards these two curriculum reform efforts.

[![](img/rosenberg.jpg){width="50%"}](https://doi.org/10.1177/23328584211024261)

[Full Paper (AERA Open)](https://journals.sagepub.com/doi/full/10.1177/23328584211024261)

#### Abstract

System-wide educational reforms are difficult to implement in the United States, but despite the difficulties, reforms can be successful, particularly when they are associated with broad public support. This study reports on the nature of the public sentiment expressed about a nationwide science education reform effort, the Next Generation Science Standards (NGSS). Through the use of data science techniques to measure the sentiment of posts on Twitter about the NGSS (*N* = 565,283), we found that public sentiment about the NGSS is positive, with only 11 negative posts for every 100 positive posts. In contrast to findings from past research and public opinion polling on the Common Core State Standards, sentiment about the NGSS has become more positive over time---and was especially positive for teachers. We discuss what this positive sentiment may indicate about the success of the NGSS in light of opposition to the Common Core State Standards.

#### Data Sources

Similar to data we'll be using for this case study, Rosenberg et al. used publicly accessible data from Twitter collected using the Full-Archive Twitter API and the {rtweet} package in R. Specifically, the authors accessed tweets and user information from the hashtag-based \#NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Data used in this case was pulled using an [Academic Research developer account](https://developer.twitter.com/en/products/twitter-api/academic-research) and the {academictwitter} package, which uses the Twitter API v2 endpoints and allows researchers to access the full twitter archive, unlike the standard developer account. Data includes all tweets from January through May of 2020 and included the following terms: `#ccss`, `common core`, `#ngsschat`, `ngss`. Below is an example of the code used to retrieve data for this lab. **This code is set not to execute and will not run**, but it does illustrate the search query used, variables selected, and time frame. For those that created a standard developer account, we will learn how to use your developer account later in this section to retrieve data from Twitter.

```{r, eval=FALSE}
library(academictwitteR)
library(tidyverse)

ccss_tweets_2021 <-
  get_all_tweets('(#commoncore OR "common core") -is:retweet lang:en',
                 "2021-01-01T00:00:00Z",
                 "2021-05-31T00:00:00Z",
                 bearer_token,
                 data_path = "ccss-data/",
                 bind_tweets = FALSE)

ccss_tweets <- bind_tweet_jsons(data_path = "ccss-data/") %>%
  select(text,
         created_at,
         author_id,
         id,
         conversation_id,
         source,
         possibly_sensitive,
         in_reply_to_user_id)


write_csv(ccss_tweets, here("data", "ccss-tweets.csv"))
```

#### Analysis

The authors determined Tweet sentiment using the Java version of SentiStrength to assign tweets to two 5-point scales of sentiment, one for positivity and one for negativity, because SentiStrength is a validated measure for sentiment in short informal texts (Thelwall et al., 2011). In addition, they used this tool because Wang and Fikis (2019) used it to explore the sentiment of CCSS-related posts. We'll be using the AFINN sentiment lexicon which also assigns words in a tweet to two 5-point scales, in addition to exploring some other sentiment lexicons to see if they produce similar results. We will use a similar approach to label tweets as positive, negative, or neutral using the {Vader} package which greatly simplifies this process.

The authors also used the `lme4` package in R to run a mixed effects model to determine if sentiment changes over time and differs between teachers and non-teachers. We won't try to replicate in this study, but we will take a look at some of their findings from this model in below.

#### **Summary of Key Findings**

1.  Contrasting with sentiment about CSSS, sentiment about the NGSS science education reform effort is overwhelmingly positive, with approximately 9 positive tweets for every negative tweet.
2.  Teachers were more positive than non-teachers, and sentiment became substantially more positive over the ten years of NGSS-related posts.
3.  Differences between the context of the tweets were small, but those that did not include the \#NGSSchat hashtag became more positive over time than those posts that did not include the hashtag.
4.  Individuals posted more tweets during \#NGSSchat chats, the sentiment of their posts was more positive, suggesting that while the context of individual tweets has a small effect (with posts not including the hashtag becoming more positive over time), the effect upon individuals of being involved in the \#NGSSchat was positive.

Finally, you can watch Dr. Rosenberg provide a quick 3-minute overview of this work at [\<https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x\>](https://stanford.app.box.com/s/i5ixkj2b8dyy8q5j9o5ww4nafznb497x){.uri}

### 1b. Define Questions

One overarching question that Silge and Robinson (2018) identify as a central question to text mining and natural language processing, and that we'll explore later in this case study, is the question:

> How do we to **quantify** what a document or collection of documents is about?

The questions guiding the Rosenberg et al. study attempt to quantify public sentiment around the NGSS and how that sentiment changes over time. Specifically, they asked:

1.  What is the public sentiment expressed toward the NGSS?
2.  How does sentiment for teachers differ from non-teachers?
3.  How do tweets posted to \#NGSSchat differ from those without the hashtag?
4.  How does participation in \#NGSSchat relate to the public sentiment individuals express?
5.  How does public sentiment vary over time?

For our text mining case study, we'll use approaches similar to those used by the authors cited above to better understand public discourse surrounding these standards, particularly as they relate to STEM education. We will also try to guage public sentiment around the NGSS, by comparing how much more positive or negative NGSS tweets are relative to CSSS tweets. Specifically, in this case study we'll attempt to answer the following questions:

1.  What are the most frequent words or phrases used in reference to tweets about the CCSS and NGSS?
2.  How does sentiment for NGSS compare to sentiment for CCSS?

### 1c. Load Libraries

#### tidytext 📦

![](images/tidytext.png){width="20%"}

As we'll learn first hand in this module, using tidy data principles can also make many text mining tasks easier, more effective, and consistent with tools already in wide use. The {tidytext} package helps to convert text into data frames of individual words, making it easy to to manipulate, summarize, and visualize text using using familiar functions form the {tidyverse} collection of packages.

Let's go ahead and load the {tidytext} package:

```{r}
library(tidytext)
```

For a more comprehensive introduction to the `tidytext` package, I cannot recommend enough the free and excellent online book, *Text Mining with R: A Tidy Approach* [@silge2017text]. If you're interested in pursuing text analysis using R post Summer Workshop, this will be a go to reference.

#### The vader Package 📦

![](img/vader.jpeg){width="20%"}\

The {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts.

To learn more about the {vader} package and its development, take a look at the article by Hutto and Gilbert (2014), [VADER: A Parsimonious Rule-based Model forSentiment Analysis of Social Media Text](http://comp.social.gatech.edu/papers/icwsm14.vader.hutto.pdf).

Let's go ahead and load the VADER library:

```{r}
library(vader)
```

**Note:** The {vader} package can take quite some time to run on a large datasets like the one we'll be working with, so in our Model section we will examine just a small(ish) subset of tweets.

#### **rtweet 📦 (Optional)**

![](img/rtweet.jpeg){width="20%"}

The {rtweet} package provides users a range of functions designed to extract data from Twitter's REST and streaming APIs and has three main goals:

1.  Formulate and send requests to Twitter's REST and stream APIs.

2.  Retrieve and iterate over returned data.

3.  Wrangling data into tidy structures.

For those that created a Twitter Developer account, load the {rtweet} package that we'll be using to accomplish all three of the goals listed above:

```{r}
library(rtweet)
```

#### Other Packages

Finally, there are a few other packages we'll need to get started. The first two should look familiar while third {wordcloud2} package is handy little package for creating interactive word clouds.

```{r}
library(tidyverse)
library(here)
library(wordcloud2)
```

## 2. WRANGLE

The importance of data wrangling, particularly when working with text, is difficult to overstate. Just as a refresher, wrangling involves the initial steps of going from raw data to a dataset that can be explored and modeled [@krumm2018]. This case study will place a heavy emphasis on preparing text for analysis and in particular we'll learn how to:

a.  **Import Tweets**. First we revisit the familiar `read_csv()` function for reading in our CCSS and NGSS tweets into R. For those of you who created a Twitter Developer Account, we'll also demonstrate the use of the {rtweet} package for downloading data directly from Twitter.
b.  **Restructure Data**. We focus on removing extraneous data using the `select()` and `filter()` functions from {dplyr}, and revisit functions from the [Tidy Your Data Primer](https://rstudio.cloud/learn/primers/4.3) for merging data frames.
c.  **Tidy Text.** Finally, we introduce the {tidytext} package to "tidy" and tokenize our tweets in order to create our data frame for analysis. We also introduce a new join function to remove "stop words" that don't add much value to our analysis.

### 2a. Part 1: Import Tweets from API (Optional)

This section is optional and for those those [setup a Twitter Developer Account and App](https://laser-institute.github.io/laser-sundry/twitter-setup.html#Introduction). The Import Tweets section introduces the following functions from the {rtweet} package for reading Twitter data into R:

-   [`create_token()`](https://cran.r-project.org/web/packages/rtweet/vignettes/auth.html) Sends request to generate authorization tokens for use of the Twitter API.
-   [`search_tweets()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Pulls up to 18,000 tweets from the last 6-9 days matching provided search terms. 
-   [`search_tweets2()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns data from **multiple** search queries. 
-   [`get_timelines()`](https://www.rdocumentation.org/packages/rtweet/versions/0.7.0/topics/search_tweets) Returns up to 3,200 tweets of one or more specified Twitter users.

#### Authorization methods

Users can create their personal Twitter token in two different ways. THe recommend method is outlined below.

-   Navigate to [developer.twitter.com/en/apps](https://developer.twitter.com/en/apps) and select your Twitter app
-   Click the tab labeled `Keys and tokens` to retrieve your keys.
-   Locate the `Consumer API keys` (aka "API Secret").

![](img/create-app-6.png)

-   Scroll down to `Access token & access token secret` and click `Create`

![](images/create-app-7.png){width="80%"}

-   Copy and paste the four keys (along with the name of your app) into an R script file and pass them along to `create_token()`. Note, these keys are named secret for a reason. I recommend setting up your token in a separate R script than the one that you will eventually share.

```{r, eval=FALSE}
## store api keys (these are fake example values; replace with your own keys)
app_name <- "Text Mining in Education"
api_key <- "afYS4vbIlPAj096E60c4W1fiK"
api_secret_key <- "bI91kqnqFoNCrZFbsjAWHD4gJ91LQAhdCJXCj3yscfuULtNkuu"
access_token <- "9551451262-wK2EmA942kxZYIwa5LMKZoQA4Xc2uyIiEwu2YXL"
access_token_secret <- "9vpiSGKg1fIPQtxc5d5ESiFlZQpfbknEN1f1m2xe5byw7"

## authenticate via web browser
token <- create_token(
  app = app_name,
  consumer_key = api_key,
  consumer_secret = api_secret_key,
  access_token = access_token,
  access_secret = access_token_secret)
```

#### Authorization in future R sessions

-   The `create_token()` function should automatically save your token as an environment variable for you. So next time you start an R session [on the same machine], rtweet should automatically find your token.
-   To make sure it works, restart your R session, run the following code, and again check to make sure the app name and `api_key` match.

```{r}
## check to see if the token is loaded
get_token()
```

That's it!

#### Search Tweets

Since one of our goals for this walkthrough is a very crude replication of the study by Rosenberg et al. (2021), let's begin by introducing the `search_tweets()` function to try reading into R 5,000 tweets containing the NGSS hashtag and store as a new data frame `ngss_all_tweets`.

Type or copy the following code into your R script or console and run:

```{r}
ngss_all_tweets <- search_tweets(q = "#NGSSchat", n=5000)
```

Note that the first argument `q =` that the `search_tweets()` function expects is the search term included in quotation marks and that `n =` specifies the maximum number of tweets

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

View your new `ngss_all_tweets`data frame using the code chunk below and answer the following questions:

```{r}
ngss_all_tweets
```

1.  How many tweets did our query using the Twitter API actually return? How many variables?

    -   

2.  Why do you think our query pulled in far less than 5,000 tweets requested?

    -   

3.  Does our query also include retweets? How do you know?

    -   

#### Remove Retweets

While not explicitly mentioned in the paper, it's likely the authors removed retweets in their query since a retweet is simply someone else reposting someone else's tweet and would duplicate the exact same content of the original.

Let's use the `include_rts =` argument to remove any retweets by setting it to `FALSE`:

```{r}
ngss_non_retweets <- search_tweets("#NGSSchat", 
                                   n=5000, 
                                   include_rts = FALSE)
```

#### Using the OR Operator

If you recall from the [Data Sources] section above, the authors accessed tweets and user information from the hashtag-based \#NGSSchat online community, all tweets that included any of the following phrases, with "/" indicating an additional phrase featuring the respective plural form: "ngss", "next generation science standard/s", "next gen science standard/s".

Let's modify our query using the `OR` operator to also include "ngss" so it will return tweets containing either \#NGSSchat or "ngss" and assign to `ngss_or_tweets`:

```{r}
ngss_or_tweets <- search_tweets(q = "#NGSSchat OR ngss", 
                                n=5000, 
                                include_rts = FALSE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Try including both search terms but excluding the `OR` operator to answer the following question:

```{r}
ngss_both_tweets <- search_tweets(q = "#NGSSchat ngss", 
                                n=5000, 
                                include_rts = FALSE)
```

1.  Does excluding the `OR` operator return more tweets, the same number of tweets, or fewer tweets? Why?

    -   

2.  What other useful arguments does the `search_tweet()` function contain? Try adding one and see what happens.

    -   

**Hint:** Use the `?search_tweets` help function to learn more about the `q` argument and other arguments for composing search queries.

#### Use Multiple Queries

Unfortunately, the `OR` operator will only get us so far. In order to include the additional search terms, we will need to use the `c()` function to combine our search terms into a single list.

The `rtweets` package has an additional `search_tweets2()` function for using multiple queries in a search. To do this, either wrap single quotes around a search query using double quotes, e.g., `q = '"next gen science standard"'` or escape each internal double quote with a single backslash, e.g., `q = "\"next gen science standard\""`.

Copy and past the following code to store the results of our query in `ngss_tweets`:

```{r}
ngss_tweets <- search_tweets2(c("#NGSSchat OR ngss", 
                                '"next generation science standard"', 
                                '"next generation science standards"', 
                                '"next gen science standard"', 
                                '"next gen science standards"' ), 
                              n=5000, 
                              include_rts = FALSE)
```

#### Our First Dictionary

Recall that for our research question we wanted to compare public sentiment about both the NGSS and CCSS state standards. Let's go ahead and create our very first "dictionary" for identifying tweets related to either set of standards, and then use that dictionary for our the `q =` query argument to pull tweets related to the state standards.

To do so, we'll need to add some additional search terms to our list:

```{r}
ngss_dictionary <- c("#NGSSchat OR ngss", 
                     '"next generation science standard"', 
                     '"next generation science standards"', 
                     '"next gen science standard"', 
                     '"next gen science standards"') 

ngss_tweets <- search_tweets2(ngss_dictionary, n=5000, include_rts = FALSE)
```

Now let's create a dictionary for the Common Core State Standards and pass that to our `search_tweets()` function to get the most recent tweets:

```{r}
ccss_dictionary <- c("#commoncore", '"common core"') 
ccss_tweets <- 
  ccss_dictionary %>% 
  search_tweets2(n=5000, include_rts = FALSE)
```

Notice that you can use the pipe operator with the `search_tweets()` function just like you would other functions from the tidyverse.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  Use the `search_tweets` function to create you own custom query for a twitter hashtag or topic(s) of interest.

#### Other Useful Queries

For your independent analysis, you may be interest in exploring posts by specific users rather than topics, key words, or hashtags. Yes, there is a function for that too!

For example, let's create another list containing the usernames of me and some of my colleagues at the Friday Institute using the `c()` function again and use the `get_timelines()` function to get the most recent tweets from each of those users:

```{r}
fi <- c("sbkellogg", "TooSweetGeek", "haspires", "tarheel93", "drcallie_tweets", "AlexDreier") 

fi_tweets <- fi %>% get_timelines(include_rts=FALSE)
```

And let's use the `sample_n()` function from the `dplyr` package to pick 10 random tweets and use `select()` to select and view just the `screenname` and `text` columns that contains the user and the content of their post:

```{r}
sample_n(fi_tweets, 10) %>% select(screen_name, text)
```

We've only scratched the surface of the number of functions available in the `rtweets` package for searching Twitter. Use the following function to learn more about the {retwee} package:

```{r, eval=F}
vignette("intro", package="rtweet")
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

To conclude Section 2a, try one of the following search functions from the `rtweet` vignette:

1.  `get_timelines()` Get the most recent 3,200 tweets from users.
2.  `stream_tweets()` Randomly sample (approximately 1%) from the live stream of all tweets.
3.  `get_friends()` Retrieve a list of all the accounts a user follows.
4.  `get_followers()` Retrieve a list of the accounts following a user.
5.  `get_favorites()` Get the most recently favorited statuses by a user.
6.  `get_trends()` Discover what's currently trending in a city.
7.  `search_users()` Search for 1,000 users with the specific hashtag in their profile bios.

### 2a. Part 2: Import Tweets from CSV

First, let's use the by now familiar `read_csv()` and `here()` functions to import our `ccss_tweets.csv` file saved in our data folder:

```{r}
ccss_tweets <- read_csv(here("unit-3", "data", "ccss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Note the addition of the `col_types =` argument for changing some of the column types to character strings because the numbers for those particular columns actually indicate identifiers for authors and tweets:

-   `author_id` = the author of the tweet

-   `id` = the unique id for each tweet

-   `converastion_id` = the unique id for each [conversation thread](https://developer.twitter.com/en/docs/twitter-api/conversation-id)

-   `in_reply_to_user_id` = the author of the tweet being replied to

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the following code chunk to import the NGSS tweets located in the same data folder as our common core tweets. By default, R will treat numerical IDs in our dataset as numeric values but we will need to convert these to characters like demonstrated above for the purpose of analysis:

```{r}
ngss_tweets <- read_csv(here("unit-3", "data", "ngss-tweets.csv"), 
          col_types = cols(author_id = col_character(), 
                           id = col_character(),
                           conversation_id = col_character(), 
                           in_reply_to_user_id = col_character()
                           )
          )
```

Importing data and dealing with data types can be a bit tricky, especially for beginners. Recall from previous case studies that RStudio has an "Import Dataset" feature in the Environment Pane that can help you use the {readr} package and associated functions to greatly facilitate this process. If you get stuck, you can copy the code generated in the lower right hand corner of the Import Dataset window.

![](images/import-data.png){width="80%"}

Now use the following code chunk to inspect the `head()` of each data frame and answer the questions that follow:

```{r}
head(ngss_tweets)
head(ccss_tweets)
```

Wow, so much for a family friendly case study! Based on this very limited sample, which set of standards do you think Twitter users are more negative about?

-   CSSS

Let's take a slightly larger sample of the CCSS tweets:

```{r}
ccss_tweets %>% 
  sample_n(20) %>%
  relocate(text)
           
```

#### [Your Turn]{style="color: green;"} ⤵

Use the code chunk below to take a sample of the NGSS tweets. Try to do it without looking at the code above first:

```{r}
ngss_tweets %>% 
  sample_n(20) %>%
  relocate(text)
```

1.  Still of the same opinion?

    -   Yes, these appear much less negative.

2.  What else you notice about our data sets? Record a few observations that you think are relevant to our analysis or might be useful for future analyses.

    -   

3.  What questions do you have about these data sets? What are you still curious about?

    -   

### 2b. Restructure Data

#### Subset Tweets

As you may have noticed, we have more data than we need for our analysis and should probably pare it down to just what we'll use.

Let's start with the CCSS tweets first. And since this is a family friendly case study, let's use the `filter()` function introduced in previous labs to filter out rows containing "possibly sensitive" language:

```{r, eval=TRUE}
ccss_tweets_1 <- ccss_tweets %>% 
  filter(possibly_sensitive == "FALSE")
```

Now let's use the `select()` function to select the following columns from our new `ss_tweets_clean` data frame:

1.  `text` containing the tweet which is our primary data source of interest
2.  `author_id` of the user who created the tweet
3.  `created_at` timestamp for examining changes in sentiment over time
4.  `conversation_id` for examining sentiment by conversations
5.  `id` for the unique reference id for each tweet and useful for counts

```{r select-variables, eval=TRUE}
ccss_tweets_2 <- ccss_tweets_1 %>% 
  select(text,
         author_id,
         created_at, 
         conversation_id,
         id)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

**Note:** The `select()` function will also reorder your columns based on the order in which you list them.

Use the code chunk below to reorder the columns to your liking and assign to `ccss_tweets_3`:

```{r}
ccss_tweets_3 <- ccss_tweets_1 %>% 
  select(id,
         text,
         author_id,
         created_at, 
         conversation_id)
```

#### Add & Relocate Columns

Finally, since we are interested in comparing the sentiment of NGSS tweets with CSSS tweets, it would be helpful if we had a column to quickly identify the set of state standards with which each tweet is associated.

We'll use the `mutate()` function to create a new variable called `standards` to label each tweets as "ngss":

```{r}
ccss_tweets_4 <- mutate(ccss_tweets_2, standards = "ccss")

colnames(ccss_tweets_4)
```

And just because it bothers me, I'm going to use the `relocate()` function to move the `standards` column to the first position so I can quickly see which standards the tweet is from:

```{r}
ccss_tweets_5 <- relocate(ccss_tweets_4, standards)

colnames(ccss_tweets_5)
```

Again, we could also have used the `select()` function to reorder columns like so:

```{r}
ccss_tweets_5 <- ccss_tweets_4 %>% 
  select(standards,
         text,
         author_id,
         created_at, 
         conversation_id,
         id)

colnames(ccss_tweets_5)
```

Before moving on to the CCSS standards, let's use the `%>%` operator and rewrite the code from our wrangling so there is less redundancy and it is easier to read:

```{r}
# Search Tweets
ccss_tweets_clean <- ccss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ccss") %>%
  relocate(standards)

head(ccss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Recall from section [1b. Define Questions] that we are interested in comparing word usage and public sentiment around both the Common Core and Next Gen Science Standards.

Create an new `ngss_tweets_clean` data frame consisting of the Next Generation Science Standards tweets we imported by using the code above as a guide.

```{r}
ngss_tweets_clean <- ngss_tweets %>%
  filter(possibly_sensitive == "FALSE") %>%
  select(text, author_id, created_at, conversation_id, id) %>%
  mutate(standards = "ngss") %>%
  relocate(standards)

head(ngss_tweets_clean)
```

#### Merge Data Frames

Finally, let's combine our CCSS and NGSS tweets into a single data frame by using the `union()` function from `dplyr` and simply supplying the data frames that you want to combine as arguments:

```{r}
ss_tweets <- union(ccss_tweets_clean,
                   ngss_tweets_clean)

ss_tweets
```

Note that when creating a "union" like this (i.e. stacking one data frame on top of another), **you should have the same number of columns in each data frame and they should be in the exact same order**.

Alternatively, we could have used the `bind_rows()` function from {dplyr} as well:

```{r}
ss_tweets <- bind_rows(ccss_tweets_clean,
                       ngss_tweets_clean)

ss_tweets
```

The distinction between these two functions is that union by default removes any duplicate rows that might have shown up in our queries.

However, since both functions returned the same number of rows, it's clear we do not have any duplicates. If we wanted to verify, {dplyr} also has an intersect function to merge the two data frames, but only where they `intersect()`, or where they have duplicate rows.

```{r}
ss_tweets_duplicate <- intersect(ccss_tweets_clean,
                                 ngss_tweets_clean)
```

#### [Your Turn]{style="color: green;"} ⤵

Finally, let's take a quick look at both the `head()` and the `tail()` of this new `ss_tweets` data frame to make sure it contains both "ngss" and "ccss" standards:

```{r}
head(ss_tweets)
tail(ss_tweets)
```

### 2c. Tidy Text

Text data by it's very nature is ESPECIALLY untidy and is sometimes referred to as "unstructured" data. In this section we learn some very useful functions from the {tidytext} package to convert text to and from tidy formats. Having our text in a tidy format will allow us to switch seamlessly between tidy tools and existing text mining packages, while also making it easier to visualize text summaries in other data analysis tools like Tableau.

#### Tokenize Text {data-link="2b. Tidy Text"}

In Chapter 1 of Text Mining with R, @silge2017text define the tidy text format as a table with one-token-per-row, and explain that:

> A **token** is a meaningful unit of text, such as a word, two-word phrase (bigram), or sentence that we are interested in using for analysis. And tokenization is the process of splitting text into tokens.

This one-token-per-row structure is in contrast to the ways text is often stored for text analysis, perhaps as strings in a corpus object or in a document-term matrix. For tidy text mining, the token that is stored in each row is most often a single word, but can also be an n-gram, sentence, or paragraph.

For this part of our workflow, our goal is to transform our `ss_tweets` data from this:

```{r}
head(relocate(ss_tweets, text))
```

Into a "tidy text" one-token-per-row format that looks like this:

```{r}
tidy_tweets <- ss_tweets %>% 
  unnest_tokens(output = word, 
                input = text) %>%
  relocate(word)

head(tidy_tweets)
```

If you take ECI 588: Text Mining in Education, you'll learn about other data structures for text analysis like the document-term matrix and corpus objects. For now, however, working with the familiar tidy data frame allows us to take advantage of popular packages that use the shared tidyverse syntax and principles for wrangling, exploring, and modeling data.

As demonstrated above, the `tidytext` package provides the incredibly powerful `unnest_tokens()` function to tokenize text (including tweets!) and convert them to a one-token-per-row format.

Let's tokenize our tweets by using this function to split each tweet into a single row to make it easier to analyze and take a look:

```{r unnest-tokens}
ss_tokens <- unnest_tokens(ss_tweets, 
                           output = word, 
                           input = text)

head(relocate(ss_tokens, word))
```

There is A LOT to unpack with this function:

-   First notice that `unnest_tokens()` expects a data frame as the first argument, followed by two column names.
-   The next argument is an output column name that doesn't currently exist but will be created as the text is "unnested" into it, `word` in this case).
-   This is followed by the input column that the text comes from, which we uncreatively named `text`.
-   By default, a token is an individual word or unigram.
-   Other columns, such as `author_id` and `created_at`, are retained.
-   All punctuation has been removed.
-   Tokens have been changed to lowercase, which makes them easier to compare or combine with other datasets (use the `to_lower = FALSE` argument to turn off if desired).

**Note:** Since {tidytext} follows tidy data principles, we also could have used the `%>%` operator to pass our data frame to the `unnest_tokens()` function like so:

```{r}
ss_tokens <- ss_tweets %>%
  unnest_tokens(output = word, 
                input = text)

ss_tokens
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

The `unnest_tokens()` function also has a specialized `“tweets”` tokenizer in the `tokens =` argument that is very useful for dealing with Twitter text. It retains hashtags and mentions of usernames with the \@ symbol as illustrated by our \@catturd2 friend who featured prominently in our the first CCSS tweet.

Rewrite the code above (**you can check answer below**) to include the token argument set to "tweets", assign to `ss_tokens_1`, and answer the questions that follow:

```{r}
ss_tokens_1 <- unnest_tokens(ss_tweets, 
                             output = word, 
                             input = text, 
                             token = "tweets")

head(ss_tokens_1)
```

1.  How many observations were our original `ss_tweets` data frame?

    -   

2.  How many observations are there now? Why the difference?

    -   

Before we move any further let's take a quick look at the most common word in our two datasets. To do so, we'll introduce the easy to use `count()` function from the {dplyr} package.

Like most functions we've introduced, the first argument `count()` expects is a data frame which we provided with the `%>%` operator, followed but the column, in our case `word`, whose values we want to count:

```{r}
ss_tokens_1 %>%
  count(word, sort = TRUE)

```

Well, many of these tweets are clearly about the CCSS and math at least, but beyond that it's a bit hard to tell because there are so many "stop words" like "the", "to", "and", "in" that don't carry much meaning by themselves.

#### Remove Stop Words

Often in text analysis, we will want to remove these stop words if they are not useful for an analysis. The `stop_words` dataset in the {tidytext} package contains stop words from three lexicons. We can use them all together, as we have here, or `filter()` to only use one set of stop words if that is more appropriate for a certain analysis.

Let's take a closer the lexicons and stop words included in each:

```{r, eval=FALSE}
View(stop_words)
```

#### The `anti_join` Function

In order to remove these stop words, we will use a function called `anti_join()` that looks for matching values in a specific column from two datasets and returns rows from the original dataset that have no matches like so:

![](img/anti-join.png)

For a good overview of the different `dplyr` joins see here: <https://medium.com/the-codehub/beginners-guide-to-using-joins-in-r-682fc9b1f119>.

Now let's remove stop words that don't help us learn much about what people are saying about the state standards.

```{r stop-unigrams}
ss_tokens_2 <- anti_join(ss_tokens_1,
                         stop_words,
                         by = "word")

head(ss_tokens_2)
```

Notice that we've specified the `by =` argument to look for matching words in the `word` column for both data sets and remove any rows from the `tweet_tokens` dataset that match the `stop_words` dataset. Remember when we first tokenized our dataset I conveniently chose `output = word` as the column name because it matches the column name `word` in the `stop_words` dataset contained in the `tidytext` package. This makes our call to `anti_join()`simpler because `anti_join()` knows to look for the column named `word` in each dataset. However this wasn't really necessary since `word` is the only matching column name in both datasets and it would have matched those columns by default.

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to take a quick count of the most common tokens in our `ss_tweets_2` data frame to see if the results are a little more meaningful, then answer the questions that follow.

```{r}
ss_tokens_2 %>%
  count(word, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

1.  How many unique tokens are in our data tidied text?

    -   73,596

2.  How many times does the word "math" occur in our set of tweets?

    -   11,788

#### Custom Stop Words

Notice that the nonsense word "amp" is among our high frequency words as well as some. We can create our own custom stop word list to to weed out any additional words that don't carry much meaning but skew our data by being so prominent.

Let's create a custom stop word list by using the simple `c()` function to combine our words. We can the add a filter to keep rows where words in our `word` column do NOT `!` match words `%in%` `my_stopwords` list:

```{r}
my_stopwords <- c("amp", "=", "+")

ss_tokens_3 <-
  ss_tokens_2 %>%
  filter(!word %in% my_stopwords)
```

Let's take a look at our top words again and see if that did the trick:

```{r}
ss_tokens_3 %>%
  count(word, sort = TRUE)
```

Much better! Note that we could extend this stop word list indefinitely. Feel free to use the code chunk below to try adding more words to our stop list.

Before we move any further, let's save our tidied tweets as a new data frame for Section 3 and also save it as a .csv file in our data folder:

```{r}
ss_tidy_tweets <- ss_tokens_3

write_csv(ss_tokens_3, here("unit-3", "data", "ss_tidy_tweets.csv"))
```

## 3. EXPLORE

Calculating summary statistics, data visualization, and feature engineering (the process of creating new variables from a dataset) are a key part of exploratory data analysis. For our first lab, we're going to keep things super simple and focus on:

a.  **Top Tokens.** Since once of our goals is to compare tweets about the NGSS and CSSS standards, we'll take a look at the to 50 words that appear in each.

b.  **Word Clouds.** To help illustrate the relative frequency each of these top 50 words occurs, we'll introduce the {wordclouds2} package for creating interactive word clouds that can be knitted with your HTML doc.

### 3a. Top Tokens

First, let's take advantage of the the `%>%` operator combine some of the functions we've used above with the `top_n()` function from the {dplyr} package. By default, this function is looking for a data frame as the first argument, and then the number of rows to return.

Let's take a look at the top tokens among the CCSS tweets by filtering our `standards` by CCSS, counting the number of times each `word` occurs, and taking the look at the 50 most common words:

```{r}
ccss_top_tokens <- ss_tidy_tweets %>%
  filter(standards == "ccss") %>%
  count(word, sort = TRUE) %>%
  top_n(50)

ccss_top_tokens
```

Not surprisingly, our search terms appear in the top 50 but the word "math" also features prominently among CCSS tweets!

### Word Clouds

Word clouds are much maligned and sometimes referred to as the "pie charts of text analysis", but they can be useful for communicating simple summaries of qualitative data for education practitioners and are intuitive for them to interpret. Also, for better or worse, these are now included as a default visualization for open-ended survey items in online Qualtrics reports and you can even add your own stop words.

The {wordclouds2} package is pretty dead simple tool for generating HTML based interactive word clouds. By default, when you pass a data frame to the `wordcloud2()` function, it will look for a word column and a column with frequencies or counts, i.e., our column `n` that we created with the `count()` function.

Let's run the `wordcloud2()` function on our `ccss_top_tokens` data frame.

```{r}

wordcloud2(ccss_top_tokens)
```

As you can see, "math" is a pretty common topic when discussing the common core on twitter but words like "core" and "common" -- which you can see better if you click the "show in a new window" button or run the code in you console -- are not very helpful since those were in our search terms when pulling data from Twitter.

In fact, search terms like these we might want to exclude from a final data product we share with with education partners or in a publication and instead include these these in a title or caption.

```{r}

ccss_top_tokens %>%
  filter(word != "common" & word != "core") %>%
  wordcloud2()
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

In the code chunk below, filter, count and select the top 50 tokens to create a word cloud for the NGSS tweets. A gold star if you can can do it without using the assignment operator or looking at the code above!

```{r}
ss_tidy_tweets %>%
  filter(standards == "ngss") %>%
  count(word, sort = TRUE) %>%
  top_n(50) %>%
  wordcloud2()
```

Also, take a look at the help file for `wordclouds2` to see if there might be other ways you could improve the aesthetics of this visualization.

## 4. MODEL

Now that we have our tweets nice and tidy, we're almost ready to begin exploring public sentiment (at least for the past week due to Twitter API rate limits) around the CCSS and NGSS standards. For this part of our workflow we introduce two new functions from the [`tidytext`](https://github.com/juliasilge/tidytext) and `dplyr` packages respectively:

#### How do you "measure" sentiment?

Sentiment analysis tries to evaluate words for their emotional association. In [Text Mining with R: A Tidy Approach](https://www.tidytextmining.com/sentiment.html), Silge aand Robinson point out that,

> One way to analyze the sentiment of a text is to consider the text as a combination of its individual words and the sentiment content of the whole text as the sum of the sentiment content of the individual words.

This isn't the only way to approach sentiment analysis, but it is an easier entry point into sentiment analysis and you'll find that is it often-used in publications that utilize sentiment analysis.

The {tidytext} package provides access to several sentiment **lexicons,** [sometimes referred to as dictionaries](https://cbail.github.io/textasdata/dictionary-methods/rmarkdown/Dictionary-Based_Text_Analysis.html), based on unigrams, i.e., single words. These lexicons contain many English words and the words are assigned scores for positive/negative sentiment, and also possibly emotions like joy, anger, sadness, and so forth.

The three general-purpose lexicons we'll focus on are:

-   `AFINN` assigns words with a score that runs between -5 and 5, with negative scores indicating negative sentiment and positive scores indicating positive sentiment.

-   `bing` categorizes words in a binary fashion into positive and negative categories.

-   `nrc` categorizes words in a binary fashion ("yes"/"no") into categories of positive, negative, anger, anticipation, disgust, fear, joy, sadness, surprise, and trust.

Note that **if this is your first time using the AFINN and NRC lexicons, you may prompted to download both** Respond yes to the prompt by entering "1" and the NRC and AFINN lexicons will download. You'll only have to do this the first time you use the NRC lexicon.

Let's take a quick look at each of these lexicons using the `get_sentiments()` function and assign them to their respective names for later use:

```{r}
afinn <- get_sentiments("afinn")

afinn
```

```{r}
bing <- get_sentiments("bing")

bing
```

```{r}
nrc <- get_sentiments("nrc")

nrc
```

And just out of curiosity, let's take a look at the `loughran` lexicon as well:

```{r}
loughran <- get_sentiments("loughran")

loughran
```

##### ✅ Comprehension Check

1.  How were these sentiment lexicons put together and validated? Hint: take a look at Chapter 2 from Text Mining with R.

    -   

2.  Why should we be cautious when using and interpreting them?

    -   

#### Come to the Dark Side

![](img/dark-side.jpeg){width="80%"}

As noted in the PERPARE section, the {vader} package is for the Valence Aware Dictionary for sEntiment Reasoning (VADER), a rule-based model for general sentiment analysis of social media text and specifically attuned to measuring sentiment in microblog-like contexts such as Twitter.

The VADER assigns a number of different sentiment measures based on the context of the entire social-media post or in our case a tweet. Ultimately, however, these measures are based on a sentiment lexicon similar to those you just saw above. One benefit of using VADER rather than the approaches described by Silge and Robinson is that we use it with our tweets in their original format and skip the text preprocessing steps demonstrated above.

One drawback to VADER is that it can take a little while to run since it's computationally intensive. Instead of analyzing tens of thousands of tweets, let's read in our original `ccss-tweets.csv` and take instead just a sample of 500 "untidu" CCSS tweets using the `sample_n()` function:

```{r}
ccss_sample <- read_csv(here("unit-3", "data", "ccss-tweets.csv")) %>%
  sample_n(500)

ccss_sample
```

Note above that we passed our `read_csv()` output directly to our `sample()` function rather than saving a new data frame object, passing that to `sample_n()`, and saving as another data frame object. The power of the `%>%` pipe!

On to the Dark Side. The {vader} package basically has just one function, `vader_df()` that does one thing and expects just one column from one frame. He's very single minded! Let's give VADER our `ccss_sample` data frame and include the `$` operator to include only the `text` column containing our tweets.

```{r}
vader_ccss <- vader_df(ccss_sample$text)

vader_ccss
```

Take a look at `vader_summary` data frame using the `View()` function in the console and sort by most positive and negative tweets.

Does it generally seem accurately identify positive and negative tweets? Could you find any that you think were mislabeled?

-   YOUR RESPONSE HERE

Hutto, C. & Gilbert, E. (2014) provide an excellent summary of the VADER package on their [GitHub repository](scores) and I've copied and explanation of the scores below:

-   The `compound` score is computed by summing the valence scores of each word in the lexicon, adjusted according to the rules, and then normalized to be between -1 (most extreme negative) and +1 (most extreme positive). This is the most useful metric if you want a single unidimensional measure of sentiment for a given sentence. Calling it a 'normalized, weighted composite score' is accurate.

**NOTE:** The `compound` score is the one most commonly used for sentiment analysis by most researchers, including the authors.

Let's take a look at the average compound score for our CCSS sample of tweets:

```{r}
mean(vader_ccss$compound)
```

Overall, does your CCSS tweets sample lean slightly negative or positive? Is this what you expected?

What if we wanted to compare these results more easily to our other sentiment lexicons just to check if result are fairly consistent?

The author's note that it is also useful for researchers who would like to set standardized thresholds for classifying sentences as either positive, neutral, or negative. Typical threshold values are:

-   **positive sentiment**: `compound` score \>= 0.05

-   **neutral sentiment**: (`compound` score \> -0.05) and (`compound` score \< 0.05)

-   **negative sentiment**: `compound` score \<= -0.05

Let's give that a try and see how things shake out:

```{r}
vader_ccss_summary <- vader_ccss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ccss_summary
```

Not quite as bleak as we might have expected according to VADER! But then again, VADER brings an entirely different perspective coming from the dark side

In a separate R script file, try using VADER to perform a sentiment analysis of the NGSS tweets and see how they compare. Post your working code in the chunk below.

```{r}
ngss_sample <- read_csv(here("unit-3", "data", "ngss-tweets.csv")) %>%
  sample_n(500)

vader_ngss <- vader_df(ngss_sample$text)

vader_ngss_summary <- vader_ngss %>% 
  mutate(sentiment = ifelse(compound >= 0.05, "positive",
                            ifelse(compound <= -0.05, 
                                   "negative", "neutral"))) %>%
  count(sentiment, sort = TRUE) %>% 
  spread(sentiment, n) %>% 
  relocate(positive) %>%
  mutate(ratio = negative/positive)

vader_ngss_summary
```

How do our results compare to the CSSS sample of tweets?

-   

## 5. COMMUNICATE

In this case study, we focused on the literature guiding our analysis; wrangling our data into a one-token-per-row tidy text format; and using simple word counts and word clouds to compare common words used in tweets about the NGSS and CCSS curriculum standards. Below, add a few notes in response to the following prompts:

1.  One thing I took away from this learning lab:

    -   

2.  One thing I want to learn more about:

    -   

Congratulations - you've completed your first text mining case study! To complete your work, you can click the drop down arrow at the top of the file, then select "Knit top HTML". This will create a report in your Files pane that serves as a record of your code and its output you can open or share.

If you wanted, you could save the processed data set to your data folder. The `write_csv()` function is useful for this. The following code is set to not run, as we wanted to ensure that everyone had the data set needed to begin the second learning lab, but if you're confident in your prepared data, you can save it with the following:

```{r, eval = FALSE}
write_csv()
```

## Extra Credit (Optional)

Using your own text data or data that you you pulled from Twitter above, try tidying your data into a tidy text format, examining the top words in your dataset, and conducting sentiment analysis with VADER.

```{r}

```

If you'd like to use the data we've been working with for extra credit, let's take a quick look at text analysis using **bigrams**, or tokens consisting of two words.

### Bigrams

So far in this lab, we specified tokens as individual words, but many interesting text analyses are based on the relationships between words, which words tend to follow others immediately, or words that tend to co-occur within the same documents.

We can also use the `unnest_tokens()` function to tokenize our tweets into consecutive sequences of words, called **n-grams**. By seeing how often word X is followed by word Y, we could then build a model of the relationships between them.

To specify our tokens as bigrams, We do add `token = "ngrams"` to the `unnest_tokens()` function and setting `n` to the number of words in each n-gram. Let's set `n` to 2, so we can examine pairs of two consecutive words, often called "bigrams":

```{r ccss-bigrams}
ngss_bigrams <- ngss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2)
```

Before we move any further let's take a quick look at the most common bigrams in our NGSS tweets:

```{r}
ngss_bigrams %>% 
  count(bigram, sort = TRUE)
```

As we saw above, a lot of the most common bigrams are pairs of common (uninteresting) words as well. Dealing with these is a little less straightforward and we'll need to use the `separate()` function from the `tidyr` package, which splits a column into multiple based on a delimiter. This lets us separate it into two columns, "word1" and "word2", at which point we can remove cases where either is a stop-word.

```{r stop-bigrams}
library(tidyr)
bigrams_separated <- ngss_bigrams %>%
  separate(bigram, c("word1", "word2"), sep = " ")

bigrams_filtered <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word)

tidy_bigrams <- bigrams_filtered %>%
  unite(bigram, word1, word2, sep = " ")
```

Let's take a look at our bigram counts now:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

Better, but there are still many tokens not especially useful for analysis.

Let's make a custom custom stop word dictionary for bigrams just like we did for our unigrams. A list is started for you below, but you likely want to expand our list off stop words:

```{r}
my_words <- c("https", "t.co")
```

Now let's separate, filter, and unite again:

```{r}
tidy_bigrams <- bigrams_separated %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")
```

Note that since `my_words` is just a vector of words and not a data frame like `stop_words`, we do not need to select the `word` column using the `$` operator.

Let's take another quick count of our bigrams:

```{r}
tidy_bigrams %>% 
  count(bigram, sort = TRUE)
```

#### [Your Turn]{style="color: green;"} ⤵ {style="font-style: normal; font-variant-caps: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-tap-highlight-color: rgba(26, 26, 26, 0.3); -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; text-decoration: none; caret-color: rgb(0, 0, 0); color: rgb(0, 0, 0);"}

Use the code chunk below to tidy and count our bigrams for the CCSS tweets:

```{r}
ccss_bigrams <- ccss_tweets %>% 
  unnest_tokens(bigram, 
                text, 
                token = "ngrams", 
                n = 2) %>%
  separate(bigram, c("word1", "word2"), sep = " ") %>%
  filter(!word1 %in% stop_words$word) %>%
  filter(!word2 %in% stop_words$word) %>%
  filter(!word1 %in% my_words) %>%
  filter(!word2 %in% my_words) %>%
  unite(bigram, word1, word2, sep = " ")

ccss_bigrams %>% 
  count(bigram, sort = TRUE)
```

What additional insight, if any, did looking at bigrams bring to out analysis?

-   YOUR RESPONSE HERE

### References

Note: Citations embedded in R Markdown will only show upon knitting.
